not for publication or presentation · not for publication or presentation. polymorphism in...

Not for publication or presentation

A G E N D A CIBMTR WORKING COMMITTEE FOR IMMUNOBIOLOGY Salt Lake City, UT Saturday, February 24, 2018, 12:15 pm– 4:45 pm

Co-Chair: Michael Verneris, MD, University of Colorado – Denver; Telephone: 303-724-4006; E-mail: [email protected]

Co-Chair: Katharina Fleischhauer, MD; Essen University Hospital; Telephone: +49-201-723-4582; E-mail: [email protected]

Co-Chair: Katharine Hsu, MD, PhD; Memorial Sloan-Kettering Cancer Center; Telephone: 646-888-2667; E-mail: [email protected]

Co-Scientific Dir: Stephanie Lee, MD, MPH, Fred Hutchinson Cancer Research Center Telephone: 206-667-6190; E-mail: [email protected]

Co-Scientific Dir: Stephen Spellman, MBS, CIBMTR Immunobiology Research Telephone: 763-406-8334; E-mail: [email protected]

Statistical Director: Tao Wang, PhD, CIBMTR Statistical Center Telephone: 414-955-4339; E-mail: [email protected]

Statisticians: Michael Haagenson, MS, CIBMTR Statistical Center Telephone: 763-406-8609; E-mail: [email protected]

12:15 pm 1. Introduction (M Verneris)a. Minutes and Overview Plan of Immunobiology Working Committee from

Tandem 2017 (Attachment 1) b. Newly appointed chair: Sophie Paczesny, MD, PhD; Indiana University Hospital;

Telephone: 317-278-5487; E-mail: [email protected]

2. Published or submitted papers 12:25 pm

a. IB08-08 Goyal RK, Lee SJ, Wang T, Trucco M, Haagenson M, Spellman SR, Veneris M,Ferrell RE. Novel HLA-DP region susceptibility loci associated with severe acute GvHD.Bone Marrow Transplant 2017 Jan 1;52(1):95-100. doi:10.1038/bmt.2016.210. Epub2016 Sep 5.

b. IB12-04 Hoff GA, Fischer JC, Hsu K, Cooley S, Miller JS, Wang T, Haagenson M, SpellmanS, Lee SJ, Uhrberg M, Venstrom JM, Verneris MR. Recipient HLA-C haplotypes andmiRNA 148a/b binding sites have no impact on allogeneic hematopoietic celltransplantation outcomes. Biol Blood Marrow Transplant 2017 Jan 1; 23(1):153-160.doi:10.1016/ j.bbmt.2016.09.028. Epub 2016 Oct 13.

c. IB13-05 Askar M, Sobecks R, Wang T, Haagenson M, Majhail N, Madbouly A, Thomas D,Zhang A, Fleischhauer K, Hsu K, Verneris M, Lee SJ, Spellman SR, Fernández-Viña M.MHC class I chain-related gene A (MICA) donor-recipient mismatches and MICA-129

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polymorphism in unrelated donor hematopoietic cell transplantations has no impact on outcomes in acute lymphoblastic leukemia, acute myeloid leukemia, or myelodysplastic syndrome: A Center for International Blood and Marrow Transplant Research study. Biol Blood Marrow Transplant 2017 March 1; 23(3):436-444. doi.org/10.1016/j.bbmt.2016.11.021. Epub 2016 Dec 14.

d. IB14-03b Lindsley RC, Saber W, Mar B, Medd, Wang T, Haagenson M, Grauman P, Zhu Z,Spellman S, Lee SJ, Verneris M, Hsu K, Fleischhauer K, Cutler C, Antin JH, Neuberg D,Ebert BL. Prognostic Mutations in Myelodysplastic Syndrome After Stem CellTransplantation. New Engl J Med 2017 Feb 9; 376(6):536-547.doi.org/10.1056/NEJMoa1611604. Epub 2017 Feb 9.

e. IB12-03 Madbouly A, Wang T, Haagenson M, Paunic V, Vierra-Green C, Fleischhauer K,Hsu KC, Verneris MR, Majhail NS, Lee SJ, Spellman SR and Maiers. Investigating theassociation of genetic admixture and donor/recipient genetic disparity with transplantoutcomes. Biol Blood Marrow Transplant 2017 June 1; 23(6):1029-1037.doi.org/10.1016/j.bbmt.2017.02.019. Epub 2017 March 2.

f. IB15-06c Wang Y, Wang T, Dagnall C, Haagenson M, Spellman SR, Hicks B, Jones K, LeeSJ, Savage SA, Gadalla SM. Relative telomere length before hematopoietic celltransplantation and outcome after unrelated donor hematopoietic cell transplantationfor acute leukemia. Biol Blood Marrow Transplant 2017 July 1;23(7):1054-1058.doi.org/10.1016/j.bbmt.2017.03.032. Epub 2017 Apr 4.

g. R04-74e Boudreau JE, Giglio F, Gooley TA, Stevenson PA, Le Luduec J-B, Shaffer BC,Rajalingam R, Hou L, Hurley CK, Noreen H, Reed EF, Yu N, Vierra-Green C, Haagenson M,Malkki M, Petersdorf EW, Spellman S, Hsu K. KIR3DL1/HLA-B subtypes govern acutemyelogenous leukemia relapse after hematopoietic cell transplantation. J Clin Oncol2017 July 10; 35(20):2268-2278. doi.org/10.1200/JCO.2016.70.7059. Epub 2017 May18.

h. IB12-02B Fleischhauer K, Ahn KW, Wang H-L, Zito L, Crivello P, Müller C, Verneris M,Shaw BE, Pidala J, Oudshorn M, Lee SJ and Spellman SR. Directionality of non-permissiveHLA-DPB1 T-cell epitope group mismatches in 8/8 matched unrelated donorhematopoietic cell transplantation. Bone Marrow Transplant.doi.org/10.1038/bmt.2017.96. Epub 2017 June 5.

i. IB13-01 Eapen M, Wang T, Veys PA, Boelens JJ, St Martin A, Spellman S, Bonfim CS,Brady C, Cant AJ, Dalle J-H, Davies SM, Freeman J, Hsu KC, Fleischhauer K, Kenzey C,Kurtzberg J, Michel G, Orchard PJ, Paviglianiti A, Rocha V, Veneris MR, Volt F, Wynn R,Lee SJ, Horowitz MM, Gluckman E, Ruggeri A. Allele-level HLA matching for umbilicalcord blood transplantation for non-malignant diseases in children: A retrospectiveanalysis. Lancet Haematol 2017 July 1;4(7):e325-e333. doi.org/10.1016/S2352-3026(17)30104-7. Epub 2017 June 13.

j. IB09-06/RT09-04b Clay-Gilmour AI, Hahn T, Preus LM, Onel K, Skol A, Hungate E, Zhu Q,Haiman CA, Stram DO, Pooler L, Sheng X, Yan L, Liu Q, Hu Q, Liu S, Battaglia S, Zhu X,Block AW, Sait SNJ, Karaesmen E, Rizvi A, Weisdorf D, Ambrosone CB, Tritchler D,

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Ellinghaus E, Ellinghaus D, Stanulla M, Clavel J, Orsi L, Spellman SR, Pasquini MC, McCarthy PL, Sucheston-Campbell LE. Genetic association with B-cell acute lymphoblastic leukemia in allogeneic transplant patients differs by age and sex. Blood Advances. 2017 Sept 8;1(20):1717-1728. doi.org/10.1182/bloodadvances.2017006023. epub 2017 Sept 12.

k. IB14-01 William BM, Wang T, Haagenson M, Fleischhauer K, Verneris M, Hsu KC, de LimaMJ, Fernandez-Vina M, Spellman SR, Lee SJ, Hill BT. Impact of human leukocyte antigen(HLA) alleles on outcomes of allogeneic transplantation for B-cell non-Hodgkinlymphomas: A Center for International Blood and Marrow Transplant Research analysis.Biol Blood Marrow Transplant. doi.org/10.1016/j.bbmt.2017.11.003. Epub 2017 Nov16.

l. IB15-06a Gadalla SM, Wang T, Loftus D, Friedman L, Dagnall C, Haagenson M, SpellmanSR, Buturovic L, Blauwkamp M, Shelton J, Fleischhauer K, Hsu KC, Verneris MR, KrstajicD, Hicks B, Jones K, Lee SJ, Savage SA. Donor telomere length and outcomes afterallogeneic unrelated hematopoietic cell transplant in patients with acute leukemia.Bone Marrow Transplant. doi.org/10.1038/s41409-017-0029-9. Epub 2017 Dec 21.

m. IB09-06/RT09-04 Ezgi Karaesmen, Abbas A. Rizvi, Leah Preus, Philip L. McCarthy,Marcelo C. Pasquini, Kenan Onel, Xiaochun Zhu, Stephen Spellman, Christopher A.Haiman, Daniel O. Stram, Loreall Pooler, Xin Sheng, Qianqian Zhu, Li Yan, Qian Liu, QiangHu, Amy Webb, Guy Brock, Alyssa I. Clay-Gilmour, Sebastiano Battaglia, David Tritchler,Song Liu, Theresa Hahn and Lara E. Sucheston-Campbell. Replication and validation ofgenetic polymorphisms associated with survival after allogeneic blood or marrowtransplant Blood 2017 :blood-2017-05-784637; doi: https://doi.org/10.1182/blood-2017-05-784637

n. IB13-08 Prediction of Acute Graft-Versus-Host Disease Following Hematopoietic CellTransplantation (Lee C, Haneuse S, Wang H, Rose S, Spellman SR, Verneris M, Hsu K,Fleischhauer K, Lee SJ, Abdi R) In Press. PLOS1.

o. IB15-06b Evaluation of a Machine Learning-Based Prognostic Model for UnrelatedHematopoietic Cell Transplantation Donor Selection. Buturovic L, Shelton J, Spellman SR,Wang T, Friedman L, Loftus D, Hesterberg L, Woodring T, Fleischhauer K, Hsu KC,Verneris MR, Haagenson M, Lee SJ. In Press. Biol Blood Marrow Transplant.

p. IB09-06/RT09-04 Exomechip Analyses Identify Genes affecting mortality after HLA-Matched Unrelated Donor Blood and Marrow Transplantation Qian Liu, Qiang Hu, LeahPreus, Alyssa I. Clay, Ken Onel, Daniel O. Stram, Loreall Pooler, Xin Sheng, Christopher A.Haiman, Xiaochun Zhu, Stephen R. Spellman, Marcelo Pasquini, Philip L. McCarthy , SongLiu, Theresa Hahn, Lara E. Sucheston-Campbell. Submitted. Blood.

q. IB10-01d Flow Cytometry using FISH techniques in a Severe Aplastic Anemia population.Gadalla S, Aubert G, Wang T, Haagenson M, Spellman SR, Wang L, Katki HA, Savage S,Lee SJ. Submitted. Blood.

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r. IB14-06 Donor-directed HLA-specific antibodies in unrelated hematopoietic celltransplantation for non-malignant disorders. Woolfrey A, Wang T, Lee SJ, HaagensonMD, Chen G, Fleischhauer K, Horan J, Hsu K, Tyan D, Verneris M, Spellman SR,Fernandez-Vina M. Submitted. Blood.

s. IB14-08 Development and validation of a clinical unrelated donor selection score. ShawBE, Logan BR, Spellman SR, Marsh SGE, Robinson J, Pidala J, Hurley C, Barker J, MaiersM, Dehn J, Wang H, Haagenson M, Porter D, Petersdorf EW, Woolfrey A, Horowitz MM,Verneris M, Hsu KC, Fleischhauer K, Lee SJ. Submitted. Biol Blood Marrow Transplant.

12:25 pm

12:35 pm

3. Research repository update and accrual tables (S Spellman) (Attachment 2)

4. Future/proposed studies and discussiona. Voting guidelines (Hsu K) b. PROP1710-09 Clonal Mosaicism and HCT Outcomes in Patients with Acute Leukemia and

Myelodysplastic Syndromes (L Sucheston-Campbell/T Hahn/S Gadalla) – (Attachment 3)c. PROP1711-97 Imputation of KIR in genome-wide association study and the association of

KIR-HLA with outcomes following alloHCT In AML and MDS (C Camacho-Bydume/L Sucheston-Campbell/S Leslie/K Hsu) – (Attachment 4)

d. PROP1711-03 Effect of HLA phenotypes on long term GVHD risk (C Story/M Riches/P Armistead) – (Attachment 5)

e. PROP1711-71 The impact of HLA class I risk alleles associated with AA Immune pathogenesis on allogeneic transplant outcomes in patients with severe acquired aplastic anemia (D Babushok/T Olson) – (Attachment 6)

f. PROP1711-06 Role of HLA allotypes in determining CMV and leukemia specific outcomes in patients undergoing unrelated donor alloHCT (B Shaffer/R Sottile/R O'Reilly/K Hsu) – (Attachment 7)

g. PROP1711-106 The Impact Of MHC Class I Chain-Related Gene A (MICA) 129 Polymorphism On CMV infection in Unrelated Donor Hematopoietic Cell Transplants(HCT) For Hematological Malignancies – Extension of Study IB13-05 (M Askar) –(Attachment 8)

h. PROP1711-79 Evaluation of the impact of donor KIR genotype on outcome after unrelated donor transplantation in patients with myelodysplastic syndromes or secondary acute myeloid leukemia – Joint study with EBMT Chronic Leukemia Working Party (J Schetelig, N Kröger, M Robin) – (Attachment 9)

i. PROP1711-128 Chromosomal aberrations and transplant outcomes in patients with inherited bone marrow failure syndromes (Y Wang/S Gadalla) – (Attachment 10)

j. PROP1711-169 The Effect of HLA Class I Heterozygosity and HLA Supertypes on Outcomes Following Allogeneic Hematopoietic Cell Transplant For Myeloid and Lymphoid Malignancies (C Camacho-Bydume/K Hsu) – (Attachment 11)

Dropped proposal (pending funding) a. PROP1710-14 Whole exome sequencing to simulate alloreactive t cell growth following

stem cell transplantation

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Dropped proposal (due to limited populations/lack of samples/lack of feasibility) a. PROP1710-04 Human leukocyte antigen B allele polymorphism and its association with

leukemia in north indian populationb. PROP1711-22 Impact of HLA-DRB1 matching on survival following HIDT with PTCy for

adults with hematologic malignanciesc. PROP1711-24 Impact of KIR ligand mismatches on clinical outcomes following

haploidentical stem cell transplantationd. PROP1711-25 Do hypomethylating agents following HLA C1/C2 mismatched or HLA/KIR

mismatched transplantation improve clinical outcome in acute myeloid leukemia?e. PROP1711-38 The relationship between HLA epitope mismatch and clinical outcomes in

haploHCT with post-transplant cyclophosphamide

BREAK – 20 minutes 2:15 pm

5. Studies in progress (Attachment 12)

NK/KIR 2:35 pm (Chair: K Hsu)

a. R02-40/R03-63 Acquisition of natural killer cell receptors in recipients of unrelated transplant (J Miller/E Trachtenberg) Ongoing – no update

b. R04-74d Functional significance of killer cell immunoglobulin-like receptor genes in HLA-matched and mismatched unrelated HCT (K Hsu) Ongoing – no update

c. IB15-02 Natural killer cell genomics and outcomes after allogeneic transplantation for chronic lymphocytic leukemia (V Bachanova, JS Miller, D Weisdorf, S Cooley) (Attachment 13) Manuscript preparation – no update

d. IB15-03 Killer Immunoglobulin Receptor (KIR) gene content and pediatric acute leukemia transplant outcomes (MR Verneris, J Miller, S Cooley) Protocol development – no update

e. IB17-02 Donor-recipient NK cell determinants associated with survival in JMML after hematopoietic stem cell transplantation (D Lee, H Rangarahan) Protocol development –no update

HLA GENES – CLASSICAL MATCHING 2:35 pm (Chair: K Fleischhauer)

a. IB12-02C Prospective assignment of HLA-DPB1 T cell epitope (TCE) group mismatches by functional distance scores compared to the functional TCE assignment algorithm (K Fleischhauer) Manuscript preparation – update

b. IB13-09 The development of machine learning based classifiers to define the alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation (Y Louzoun) Manuscript preparation – no update

c. IB14-08 Development and validation of a clinical unrelated donor selection score. BE Shaw/SJ Lee) (Attachment 14) Submitted – no update

d. IB15-01 The impact of single nucleotide gene polymorphisms (SNP) in the gamma block of the major histocompatibility complex (MHC) on unrelated donor hematopoietic cell transplants (HCT) for hematological malignancies (M Askar/R Sobecks) (Attachment 15) Manuscript preparation – Update

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e. IB16-01 The role of HLA-E compatibility in the prognosis of acute leukemia patients undergoing 10/10 HLA matched unrelated HSCT (C Tsamadou/D Fürst/J Mytilineos) Sample typing – no update

f. IB16-02 Use of HLA structure and function parameters to understand the relationship

between HLA disparity and transplant outcomes (LA Baxter-Lowe) Data file preparation –

no update

CYTOKINE/CHEMOKINE 2:55 pma. IB14-03a: The prognostic impact of somatic mutations and levels of CXC chemokine

ligands on post hematopoietic cell transplantation (HCT) outcomes in patients with myelodysplastic syndromes (MDS) (W Saber/B Dhakal) Manuscript preparation – no updateOTHER GENES 2:55 pm(Chair: K Fleischhauer)

g. IB06-05 Use of high-resolution HLA data from the NMDP for the International Histocompatibility Working Group in HCT (E Petersdorf) Ongoing – no update

h. IB09-04 D/R gene polymorphisms of drug metabolisms and innate immune response post allele matched unrelated donor HCT (V Rocha) Manuscript preparation – no update

i. IB09-06/RT09-04b Genetic susceptibility to transplant-related mortality after unrelated donor stem cell transplant (T Hahn/L Sucheston-Campbell) (Attachment 16) Ongoing –Update

j. IB10-01c Chromosome 6 Loss-of-heterozygosity in Pre-transplant Blood Samples of Patients with Severe Aplastic Anemia is Associated with Lower Risk of Acute Graft-versus-Host Disease (S Gadalla) (Attachment 17) Manuscript preparation – no update

k. IB10-01d Flow Cytometry using FISH techniques in a Severe Aplastic Anemia population.(S Gadalla) (Attachment 18) Submitted

l. IB14-04 Assessing the similarity of the T cell receptor repertoire in allogeneic hematopoietic stem cell recipients with the same single human leukocyte mismatches(EH Meyer) Manuscript preparation – no update

m. IB14-05 mtDNA haplotypes and unrelated donor transplant outcomes (M Verneris/J Ross) Analysis – no update

n. IB15-04 Clinical outcomes among hematopoietic stem cell transplant recipients as a function of socioeconomic status and related transcriptome differences (J Knight, JD Rizzo/S Cole) (Attachment 19) Manuscript preparation – No update

o. IB15-05 Secondary findings in exome sequencing data (S Savage/S Gadalla) Manuscript preparation – no update

p. IB15-07 Functional genetic variants of the ST2 gene in pairs of recipient and donors for risk stratification of GVHD and TRM outcomes (S Paczesny) (Attachment 20) Manuscript preparation – Update

q. IB16-03 Role of recipient and donor genetic polymorphisms in interferon lambda 4(INFL4) on outcomes after unrelated allogeneic cell transplant (S Gadalla) Manuscript preparation – Update

r. IB17-03 Identification of genomic markers of post hematopoietic cell transplantation(HCT) outcomes in patients with myelofibrosis: A pilot study (W Saber / S Gadalla) Sample typing – no update

s. IB17-04 Epigenetic profiling of unrelated donor-recipient pairs to improve donor selection during HCT transplants (S Beck/K Peggs/V Rakyan/A Webster) Sample typing–

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no update SENSITIZATION/TOLERANCE 3:20 pm

(Chair: K Fleischhauer) a. IB11-01a Analysis of the NIMA effect on the outcome of unrelated PBSC/BM

transplantation (G Ehninger/JJ van Rood/A Schmidt) Manuscript preparation – no update b. IB11-01b Analysis of the IPA effect on the outcome of unrelated PBSC/BM

transplantation (G Ehninger) Data Collection/Data File Preparation – no update c. IB14-07 Indirectly recognizable HLA epitopes (PIRCHES): a retrospective validation study

on the role of indirect recognition of mismatched HLA in hematopoietic stem cell transplantation outcome (E Spierings) Manuscript preparation – no update

7. Deferred studies pending accrual/funding

a. IB13-06 Role of the complement system in graft-versus-host disease (V Afshar-Kharghan/J Belmont/C Amos) Pending funding

b. IB13-07 Impact of donor signal-regulatory protein alpha (SIRPα) polymorphism on outcome of allogeneic hematopoietic stem cell transplantation (allo-HSCT) (A Gassas/J Danska/S Rajakumar) Pending funding

c. IB17-01 The impact of HLA-DPB1 level of expression on clinical outcomes of transplantation (M Askar/M Fernandez-Vina) Pending funding

8. Closing remarks (K Hsu) 3:25 pm

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Not for publication or presentation Attachment 1

MINUTES CIBMTR WORKING COMMITTEE FOR IMMUNOBIOLOGY Orlando, Florida Wednesday, February 22, 2017, 12:15 pm– 4:45 pm

Co-Chair: Michael Verneris, MD, University of Colorado – Denver; Telephone: 303-724-4006; E-mail: [email protected]

Co-Chair: Katharina Fleischhauer, MD; Essen University Hospital; Telephone: +49-201-723-4582; E-mail: [email protected]

Co-Chair: Katharine Hsu, MD, PhD; Memorial Sloan-Kettering Cancer Center; Telephone: 646-888-2667; E-mail: [email protected]

Co-Scientific Dir: Stephanie Lee, MD, MPH, Fred Hutchinson Cancer Research Center Telephone: 206-667-5160; E-mail: [email protected]

Co-Scientific Dir: Stephen Spellman, MBS, CIBMTR Immunobiology Research Telephone: 763-406-8334; E-mail: [email protected]

Statistical Director: Tao Wang, PhD, CIBMTR Statistical Center Telephone: 414-955-4339; E-mail: [email protected]

Statisticians: Michael Haagenson, MS, CIBMTR Statistical Center Telephone: 763-406-8609; E-mail: [email protected]

1. Introduction (M Verneris)

Dr. Michael Verneris opened the Immunobiology Working Committee (IBWC) session at 12:20PM. The leaders of the IBWC were introduced. Membership to a working committee (WC) isautomatically assigned when your badge is scanned at the Tandem IBWC meetings. It is alsoopen to any individual willing to take an active role in the WC studies. Young investigators areencouraged to engage in and perform research. The WC goal is to publish high impact studies ina timely manner. Expectations of members are to provide the current status of ongoing studiesand timelines, and to assess and select proposals that will have a high impact on the BMT field.Limitations of studies include statistical time and the availability of appropriate sample types.

Voting for proposals is assessed on a scale from 1 as highest score to 9 as lowest score.Questions to keep in mind while voting are: 1) Will the study enhance our understanding toallow development of better HCT strategies or significantly change practice? ; 2) Is the questionand answer time-sensitive? ; 3) Is the CIBMTR the only (or best) way to address this question?;and, 4) Are the necessary data on hand or easily obtainable?

Presentations for proposals are limited to 5 minutes with 10 additional minutes for discussion.IBWC will only accept 2 or 3 proposals this year. The decisions of acceptance or declination willbe out within a month from the Tandem IBWC meeting. Prioritization will be influenced byvoting to quantify the opinion in the room, by what is the current WC portfolio and by the highimpact on the BMT field.

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Authorship on manuscripts is dependent on the substantial contributions to all parts of the study.

Additional information can be found on the CIBMTR website for participation and information

about the CIBMTR Working Committees. There are two main sources of HCT data within CIBMTR; Transplant Essential Data (TED) and

Comprehensive Report Forms (CRF). The cases with CRF data have more collected data and have extensive outcome data. A graph was shown how these two data sources are broken down.

2. Published or submitted papers

Further discussion mentioned 16 studies that have been published or submitted within the last year, in journals such as New England Journal of Medicine, Molecular Genetics & Genomic Medicine, Haematologica, Journal of Clinical Investigation, British Journal of Hematology, Biology of Blood and Marrow Transplantation and Bone Marrow Transplantation.

3. Research repository update and accrual tables (S Spellman) Mr. Steve Spellman discussed the Research Sample Repository. There are unrelated donors, cord blood, and transplant recipient pre-transplant/pre-conditioning samples from NMDP facilitated transplants in the Repository. There are also related donor/recipient samples under SCTOD with currently 65 sites participating. (These are BMTCTN Core and Ancillary centers.) More than 11,750 sample aliquots were shipped in 2016, and more than 2.3 million aliquots are stored. Currently, the Repository receives approximately 20 milliliters (ml) of whole blood from donors and recipients, and any source of DNA from infused Cord Blood Units (CBUs). These samples are frozen aliquots at -80⁰C or liquid nitrogen, and whole blood is spotted on filter paper. The Repository also receives 2 ml of ACD-A plasma as well as 2.5 ml of serum and 2.5 ml EDTA plasma for all donors from BMTCTN centers. Prior to 2002, samples of B-LCL, PBMC, granulocytes and serum were collected. There are more than 37,000 URD pairs overall, with 26,393 being first transplants, and most of those with high resolution HLA typing. There are 3887 UCBT pairs/triplets/quads overall with 3649 UCBT first transplants (single, double and triplets.) There are 5770 related paired samples, with demographic data on 4473 first allogeneic transplant pairs. Investigators are required to submit the interpreted results of all testing to the CIBMTR. This will make the data available to the HCT research community, and eliminates or reduces duplicative testing to preserve resources/sample inventory. These data are captured in the CIBMTR Data Warehouse and are linked to the sample inventory. Retrospective high resolution HLA and KIR typing are performed on stored donor/CBU-recipient pairs. This confirms sample identity and adds key immunogenetic data to the CIBMTR Database. In 2016, more than 1300 related and more than 3200 unrelated pairs were added to the Database. There are currently more than 25,000 pairs with HLA data and more than 18,000 with KIR presence/absence data.

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Mr. Spellman mentioned five studies with working committee or collaborative study testing that are currently in progress. He also mentioned the BMT CTN Biospecimen Collections available. For general information, there is the BMT CTN public website; for transplant trials, there are primarily post-transplant biospecimens supporting biomarker and cellular immune reconstitution studies; and for GvHD treatment trials, there are pre-treatment and post-treatment biospecimens supporting primarily biomarker studies. There is one additional study in collaboration with the Regimen Related Toxicity working committee (RTWC) where data sharing has already been helpful. It is an R01 GWAS study led by Dr. Theresa Hahn, and it will give the necessary SNP data for ST2 gene typing for IB15-07, which is led by Dr. Sophie Paczesny.

4. Future/proposed studies and discussion

a. Voting guidelines (Hsu K) Dr. Katharine Hsu presented the voting guidelines. She explained that a ranking of “1” is the highest score and “9” is the lowest score. Voting results will help the IBWC leadership decide which proposals should move forward.

b. PROP1612-05 The impact of HLA-DPB1 levels of expression on clinical outcomes of HCT (M Askar/M Fernandez-Viña)

Dr. Medhat Askar presented this proposal. The hypothesis is GvHD risk correlates with the level of expression of HLA-DPB1 antigen predicted by one or more of multiple polymorphisms of the mismatched HLA-DPB1 allele(s) of the recipients and/or their unrelated donors. Some background for this study is HLA-DPB1 mismatches occur in 85% of HCT, HLA-DPB1 mismatches are associated with GvHD, HLA-DPB1 regulatory region variant rs9277534 is associated with HLA-DPB1 expression (G:high/A:low), and there is more acute GvHD (aGvHD) and non-relapse mortality (NRM) with high expression. Specific aims of this proposal are to investigate the association between level of expression of HLA-DPB1 predicted by whole gene NGS and the primary outcomes of aGvHD III-IV or any aGvHD, NRM as well as secondary outcomes of relapse, complete donor chimerism, neutrophil & platelet reconstitution, chronic GvHD (cGvHD) (any & extensive), and overall survival (OS). Other specific aims include: a) analyzing the linkage disequilibrium (LD) among different genetic variance predicting HLA-DPB1 level of expression; b) validating whether HLA-DPB1 alleles are consistently carrying sequences associated with low or high expression; c) investigating the association of each variant published or identified by the PI in association with HLA-DPB1 level of expression with the study outcomes; d) populating the CIBMTR dataset with high quality allele level typing for future studies; and e) populating the IMGT database with full gene sequence for the majority of DPB1 alleles. This study will require samples. A question was raised as to why there is a need for donor chimerism as an outcome. Another question was raised asking how this study is different

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than other DPB1 expression studies. A comment by Dr. Askar was that all covariates will be analyzed for this study, and that additional variants will be analyzed in association with the known expression variants. It was asked if the investigators will define low vs. high expression and the answer was no. Another question was what difference in aGvHD rates would be clinically meaningful. A percentage difference estimate of 10-15% was suggested.

c. PROP1611-158 Identification of genomic markers of post hematopoietic cell transplantation

(HCT) outcomes in patients with myelofibrosis (MF): A pilot study (W Saber/S Gadalla)

Dr. Shahinaz Gadalla presented this proposal. The median life expectancy of patients with myelofibrosis (MF) is 4-5 years, but the prognosis varies considerably. Genomic studies identified associations between MF risk, leukemia progression, and/or mortality with: a) somatic mutations in JAK2, CALR, and MPL; (Other genes included LNK, CBL, TET2, ASXL1, IDH, IKZF1, EZH2, DNMT3A, TP53, SF3B1, SRSF2, or U2AF1); b) chromosomal abnormalities involving JAK2 (9p CN-LOH, or gain), RB (13q14) or NF1 (17q11); and c) TERT variant rs2736100 A>C SNP, and short telomere length (imetelstat, a telomerase inhibitor showed promising therapeutic effect.) The hypothesis and study design state that pre-HCT genomic alterations in MF patients (somatic mutations, genetic variations in telomere-biology genes, and chromosomal abnormalities) are prognostic for post-HCT outcomes. Planned laboratory assays include whole exome sequencing and targeted gene panels to identify somatic mutations, genotyping arrays (such as HumanOmniExpress-12v1.1 beadchip) for telomere gene SNPs and chromosomal alterations, and monoplex qPCR assay for telomere length. The statistical analysis will use Cox regression models with the following assumptions for statistical power: For N=300, type I error=0.05, power=80%, 30% of the patients exposed, the investigators would be able to detect a minimal Hazard Ratio of 1.6. The current study population has 393 cases with samples available, with a median age of 55 (range 1-73), 90% Caucasian, 78% PBSC grafts, 49% myeloablative HCTs, 74% primary MF and years of transplant 1990-2015 (88% were beyond 2000.) A question was raised wondering if all mutations are going to be analyzed. The investigators will be using whole exome sequencing so multiple abnormalities are anticipated. The goal is to take findings forward to prospective studies if strong associations are observed.

d. PROP1611-41 Donor-recipient NK cell determinants associated with survival in JMML after hematopoietic stem cell transplantation (DA Lee/HG Rangarajan) Dr. Hemalatha Rangarajan presented this proposal. The only known curative therapy for juvenile myelomonocytic leukemia (JMML) is HCT where 5-year event-free survival (EFS) is 52%. In myeloid malignancies, the impact of NK cells on survival is associated with increased numbers of NK cells in the stem cell graft, rapid recovery of NK cells in the early post-transplant period, alloreactivity of NK cells in the GvH direction predicted by KIR-ligand (HLA) mismatch, and other measures (such as activating KIR gene content, assigned KIR genotypes, and presence/absence of specific KIR genes.) However, there are no reports of

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the impact of NK cell factors on survival of JMML. This raises the question: Is JMML a NK responsive disease? This would lead to therapeutic implications for HCT. Mature monocytic population of JMML cells express similar profiles of NK cell ligands as normal healthy donor monocytes and are relatively resistant to NK cell cytotoxicity. JMML stem cells, defined as Lin-CD34+CD38-, express ligands for NKG2D, NKp30, and NKp44 more than AML stem cells. JMML stem cells also express similar levels of HLA Class I and HLA-E, suggesting that KIR mismatch may be relevant for JMML in a manner similar to AML. The hypothesis of this proposal is immunologic mechanisms are a major component of protection from relapse after HCT in patients with JMML, and specifically that determinants of greater NK cell function (e.g., KIR mismatch, KIR A/B genotype) are associated with reduced relapse. The primary objective is to compare outcomes of JMML patients treated with HCT as stratified by NK-related factors (e.g., KIR Ligand mismatch, Donor KIR B genotype) that have previously been associated with survival of other myeloid malignancies. The outcomes of Interest include 2 year EFS, OS, relapse free survival (RFS), and GvHD free-relapse free survival (GRFS). Secondary objectives are to evaluate incidence of aGvHD and cGvHD. The latest table shows N=465 with 44 having KIR typing currently available. 162 are UCBT while 101 are related donor and 202 are unrelated donor transplants. A high majority are myeloablative conditioning regimen intensity. Methods and strategies for analysis include: 1) KIR typing on donors at Dr. Dean Lee’s lab using Miltenyi SSP=PCR kits; 2) All available donors’ samples (related, URD and UCBT) will be typed for donor KIR typing, KIR A and B haplotype, B content score and number of activating/inhibiting KIR; 3) All recipients for KIR ligand assessment based on HLA-C and Bw4 presence; and 4) Analyze outcomes of interest based on a) presence of ligands (all present vs. any one present, etc.), b) presence of activating KIR receptors, c) donor KIR B content score, and d) KIR mismatch direction. The costs of this project include the reagent for KIR typing using Miltenyi SSP-PCT kits, with a purchase of reagents at the cost of $20 per sample; and sample acquisition costs, which is approximately $20 each at the government/non-profit rates. Funding sources available to the Co-investigator include a) institutional startup funds from NCH (a non-government research institution), b) philanthropy sources, and c) if proposal is approved, will submit to JMML foundation. Other funding that will be explored include the IBWC research grant mechanism to cover the costs for approved CIBMTR studies. The investigators do not know of another large JMML cohort to establish a baseline for comparison. Matching for unrelated donor transplants will likely be 7/8s and 8/8s. It was suggested to remove small groups in the tables for a more homogeneous population. Molecular markers may not be available. The question was raised if we have significant power to do this study. The study will examine missing KIR ligands determined by recipient HLA and missing self, determined by mismatched donor-recipient HLA.

Dropped proposals (due to overlap with current studies) a. PROP1608-02 Outcomes following umbilical cord blood transplantation for inherited

metabolic disorders: do allele-level HLA matching characteristics matter?

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b. PROP1611-80 Using donor characteristics and matching at low expression loci to identify optimal 7of 8 HLA matched unrelated adult donors

Dropped proposals (due to limited populations) a. PROP1611-37 Do hypo-methylating agents following HLA C1/C2 mismatched or HLA/KIR

mismatched transplantation improve clinical outcome in acute myeloid leukemia? Dropped proposals (due to feasibility/lack of samples) a. PROP1611-164 Insulin-like growth factor 1 and outcome after myeloablative allogeneic

HSCT

The dropped proposals were either repetitive of other studies, low in number of cases, or lacked samples and feasibility.

KIR/Haploidentical studies a. Discussion about current numbers, sample availability, and planning for future studies

i. PROP1610-18 Role of KIR ligand mismatches for transplant outcomes in T-cell replete, non-myeloablative haploidentical transplants, inferring KIR ligand mismatches from HLA-A, -B, and -C locus typing mismatches: a CIBMTR registry analysis

ii. PROP1611-36 Impact of KIR ligand mismatches on clinical outcomes following haploidentical stem cell transplantation

iii. PROP1612-04 KIR and HLA Allotypes and Donor NK Alloreactivity in HLA-Haploidentical Transplantation

A discussion was held about KIR typing and outcomes of haploidentical transplants. At this time, there are insufficient numbers to pursue this set of proposals, but the demand for results clearly exists. The available population is shown in attachment 6 of the IBWC agenda, restricted to those who received post-transplant cytoxan. NK immunogenetics (iKIR/HLA) dictate NK education and alloreactivity. Missing self means lack of class I KIR ligand in the target promotes NK activation. KIR ligand incompatibility in the GvH direction is associated with lower AML relapse and higher overall survival in haploidentical HCT (according to Perugia in cases of CD34-selected and ATG use.) The aims of these three proposals varied. For Proposal 1610-18, aims include looking for presence/absence of KIR ligand mismatching in the GvH direction and haploidentical HCT outcome. Such outcomes include OS, DFS, NRM, GvHD and graft failure. The study population would include non-myeloablative conditioning regimen haploidentical transplants in hematological malignancies, such as leukemia, MPD, lymphoma and multiple myeloma.)

For Proposal 1611-36, the presence/absence of KIR ligand mismatching in the GvH direction and haploidentical HCT outcome would also be considered. Here the outcomes would be considered by direction, with relapse, TRM, OS and GvHD in the GvH direction, and graft rejection in the HvG direction. Here the study population would be haploidentical HCT in all hematological malignancies (AML, MDS, ALL, MPD, CLL and lymphoproliferative disease.)

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For Proposal 1612-04, aims include determining missing self vs. non-missing self, and checking outcomes of relapse and OS within disease groups, which can be inferred from HLA alone. For non-missing self, the impact of inhibitory strength between KIR and HLA on disease relapse would be another aim, but this requires KIR/HLA allele typing. Other outcomes to be considered would be overall survival, GvHD and infection. Some power calculations for this set of proposals show that to achieve 80% power based on the log-rank test at a significance level of 0.05 are: For relapse, a 20% difference needs a sample size of 605, a 25% difference in relapse rate needs a sample size of 475, and a 30% difference in relapse rate needs a sample size of 390. A sample size would likely need to be 1000 cases to prove powered for all three proposals. In attachment 6 of the Tandem IBWC agenda, the table shows there are 453 Haploidentical HCTs available (133 Myeloablative and 320 Non-myeloablative) from the various hematological malignancy diseases. There are 123 Donor/recipient paired samples and 4 donor only samples available right now for proposal 1612-04. The recommended prioritization of studies for these three proposals would be to have a KIR ligand-based study in the overall patient population, with outcomes of OS, engraftment, GvHD and NRM; then go with a disease-stratified portion of AML vs. Lymphoid vs. other, with the outcomes of relapse and DFS. Finally, a third portion should be the KIR/HLA allele-based study with the same outcomes. (This will take time to be sure we have enough KIR allele data for this study.) The audience discussed approaches to speed data collection to allow these analyses. One suggestion was to get bigger centers to submit samples and data. Since the third proposal will have KIR allele data, can we do a bunch of KIR typing all at once? These solutions were felt to be impractical. Instead, for haploidentical transplant typing, the CIBMTR is trying to increase samples and KIR typing. It will be a few more years to accrue enough patients and to have sufficient follow-up to do these analyses but the accrual will be re-evaluated each year.

5. Studies in progress

NK/KIR (Chair: K Hsu)

a. R02-40/R03-63 Acquisition of natural killer cell receptors in recipients of unrelated transplant (J Miller/E Trachtenberg/S Cooley)

Dr. Sarah Cooley presented the update for the NK P01 Program Project grant led by the University of Minnesota. Progress for 2016 included a) the KIR sequencing method PING, which is a high throughput, Illumina based method to determine KIR alleles and copy number (published by Dr. Norman in the Journal of Human Genetics), b) the completed enrollment to the KIR Donor Selection Trial (the follow-up and sample collection is ongoing), c) KIR in allogeneic HCT for NHL (showing KIR B donors are associated with improved outcome after HLA matched HCT for NHL (published by Bachanova in BBMT 2016), and d) HLA-B dimorphism affects HLA-C

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expression and education of NKG1A/CD94+ NK cells (via HLA-C or –E, published by Amir Horowitz in Science Immunology 2016.)

Plans for 2017 include AML analyses, with a) prospective KIR DS outcome and correlative samples and b) role of KIR (alleles) in URD for AML by using an expanded cohort of 2397 old and 3317 new to look at myeloablative vs. RIC, HLA matched vs. mismatched, modern preparative regimens, CMV, and HLA-B dimorphism. Other plans include 1) BMT CTN KIR 0201, which is a KIR alllele sequence to supplement analysis of the role of PB vs. BM on NK reconstitution/function and HCT outcome; 2) CLL by Veronika Bachanova – The samples are being typed (N=574); and 3) Ph+ ALL by Veronika Bachanova (not sure about N yet).

Looking at the potentially new 3317 AML cases for the AML analyses, the new cases look older, patients are less fit, pairs are better HLA matched, more PBSC, more reduced intensity, older donors, more early disease, more CMV positive and more reflective of current practices.

It was suggested to look at specific HLA alleles and KIR interactions.

b. R04-74 Functional significance of killer cell immunoglobulin-like receptor genes in

HLA-matched and mismatched unrelated HCT (K Hsu) Ongoing – update

Dr. Katharine Hsu presented this update. Regarding the KIR3DL1/HLA-B allele study, a novel KIR3DL1 allele/subtyping methodology was developed. The study group was 1328 AML patients that were 9/10 or 10/10 matched URD HCT. Inhibition levels were tested as high vs. low/no. Also shown was an additive beneficial effect to 2DS1/HLA-C1. In vitro studies support retrospective findings. It was shown that inhibitory KIR3DL1 and HLA-B subtype combinations differentially impact AML relapse and survival, particularly among donors/recipients with both KIR2DL ligands present. Another study at Memorial Sloan-Kettering is the selection of allogeneic hematopoietic cell donors based on KIR and HLA genotypes. It is a single center prospective trial looking at high KIR3DL1 inhibition vs. low/no inhibition, currently with 252 patients and 941 donors tested. Without selection, donors have 27% disadvantageous typing, so with only one donor typed, that is the disadvantageous rate. However, the rates drop as more donors are typed: 2-3 donors is 12%, > 3 donors is 4% with p<0.0001. There is no difference in time to BMT. 115 patients underwent transplant. There is a multicenter trial open and accruing currently. Other current KIR/HLA studies include: 1) KIR2DL1, 2DL2, 2DL3 where novel allele typing methods are being developed; 2) identification of similar inhibitory hierarchies with KIR2DL1 and KIR2DL3; 3) an expanded cohort, which includes 400 additional donor samples that have been received and anticipate an additional 700; and 4) donor selection algorithm editing to include additional hierarchies.

Some questions and concerns brought up include: A) Is high, medium and low affinity associated with HLA-Bw4 allele? B) How does Bw4 affect NK licensing? C) All donors are 9/10 or 10/10, but most of the 9/10s are mismatched at non-KIR loci.

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c. IB15-02 Natural killer cell genomics and outcomes after allogeneic transplantation

for chronic lymphocytic leukemia (V Bachanova, JS Miller, D Weisdorf, S Cooley) Sample typing – no update

d. IB15-03 Killer Immunoglobulin Receptor (KIR) gene content and pediatric acute

leukemia transplant outcomes (MR Verneris, J Miller, S Cooley) Protocol development – no update

BREAK for 30 minutes at 2:15 pm.

HLA GENES – CLASSICAL MATCHING (Chair: K Fleischhauer)

a. IB12-02C Prospective assignment of HLA-DPB1 T cell epitope (TCE) group mismatches by functional distance scores compared to the functional TCE assignment algorithm (K Fleischhauer) Manuscript preparation – update

Dr. Katharina Fleischhauer presented this study update. T-cell epitope (TCE) groups for HLA-DPB1 matching were initially defined by looking at cross-reactivity of T-cells alloreactive to DPB1*09:01, resulting in three TCE groups 1, 2 and 3. This allows classification of only 72 out of more than 600 DPB1 alleles. Consequently, about 15% of unrelated donor-recipient pairs cannot be not assigned as TCE permissive or non-permissive based on the 72 alleles. This TCE assignment was initially used in the papers from Zino 2004 and Pidala 2014 and is therefore referred to as “TCE Zino/Pidala” algorithm

Functional Distance (FD) has been studied as the median impact of polymorphism at 11 amino acid (AA) in DPB1*09:01 on T-cell alloreactivity. FD scores include Fdaa as the FD scores for each AA substitution and FDallele=the sum of Fdaa in a DPB1 allele. It was shown that FDallele predicts the TCE group of any DPB1 allele. Therefore, now all over 600 DPB1 alleles can be classified according to TCE, making it possible also to classify all unrelated donor-recipient pairs including the 15% that previously could not be assigned. This is the Crivello publication in BBMT 2015. This TCE assignment is therefore referred to as “TCE Crivello” algorithm.

The impact of AA polymorphism was also checked using the “Delta of FDallele scores in patient and donor (∆FD)”. There is an 80% overlap of this classification with non-permissive TCE. In a monocentercohort from the University of Essen, it was shown that a ∆FD > 2.665 was associated with OS and TRM. This cohort used PBSC to over 90%, and about half of the transplants received ATG. (Crivello in Blood 2016.)

Aims of this study are to look for differences in the DPB1 TCE mismatch algorithms (TCE Zino/Pidala vs. TCE Crivello), as well as to evaluate the ∆FD mismatches in the independent and much larger CIBMTR cohort.

Results showed that between Zino/Pidala vs. Crivello, there is an 80% overlap, with both being significant for OS and TRM. TCE Zino/Pidala is superior for DFS while TCE

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Crivello is superior for cGvHD and aGvHD II-IV. TCE Crivello also assigns all HLA-DPB1 alleles. The conclusion here is that TCE Crivello can be recommended for clinical use.

When comparing TCE vs. ∆FD, there is an 80% overlap between ∆FD > 2.665 and TCE. When analyzing ∆FD > 2.665, it is only significant for TRM. TCE Crivello is therefore superior to ∆FD > 2.665 (OS and GvHD) in the CIBMTR cohort. ∆FD as a linear variable is significant for OS, DFS and TRM. ∆FD > 1.64 is similar to TCE Crivello (validation pending in an independent cohort). Experimental HLA-DPB1 TCE not inferior to ∆FD, but ∆FD might be more broadly applicable for risk stratification than TCE.

A question was raised wondering if there was evidence of different susceptibility to infections depending on HLA-DPB1 TCE or DFD matching status. Moreover, it was asked whether HLA-DPB1 antigens are differentially presented among the different TCE, and whether the FD score apply to other loci. The answer was that all these are excellent questions which are being investigated. Susceptibility to infection may be hard to address at CIBMTR because of missing Registry data, but is being investigated in Essen. Peptide processing appears indeed to be different in DP from different TCE groups but these data are preliminary. Application of FD score to other loci is also an interesting question but it requires time to generate the relevant experimental data.

b. IB13-01 Effect of allele-level HLA-matching after UCB HCT for non-malignant

diseases in children (P Veys/M Eapen) Analysis – no update

c. IB13-08 Short and long term survival assessment of post-HSCT transplantation using predictive modeling on a Bayesian network framework (R Abdi/S Haneuse/C Lee) Manuscript preparation – no update

d. IB13-09 The development of machine learning based classifiers to define the

alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation (Y Louzoun) Manuscript preparation – no update

e. IB14-01 Impact of human leukocyte antigen haplotypes on outcomes of allogeneic

transplantation for B-cell non-Hodgkin lymphomas (B William/M de Lima/M Fernandez-Vina/B Hill) Manuscript preparation – no update

f. IB14-02 Structural/Functional models of HLA for data mining of permissive

mismatching in allogeneic hematopoietic stem cell transplantation (L Gragert) Manuscript preparation – no update

g. IB14-08 Development and validation of a clinical unrelated donor selection score (B

Shaw, SJ Lee) Analysis – update

Dr. Stephanie Lee presented this update summary. The goal for this study is to identify donor factors associated with patient outcomes in an 8/8 population to try to develop a donor selection algorithm followed by extension into the 7/8 population. Unfortunately, the most recent attempt to validate the algorithm that was developed based on an initially discovery cohort failed, but the results were

17


returned to the PIs right before Tandem and there were some concerns about the analysis. The PIs will review the results and report back to the writing committee.

h. IB15-01 The impact of single nucleotide gene polymorphisms (SNP) in the gamma

block of the major histocompatibility complex (MHC) on unrelated donor hematopoietic cell transplants (HCT) for hematological malignancies (M Askar, R Sobecks) Sample typing – no update

i. IB16-02 Use of HLA structure and function parameters to understand the relationship between HLA disparity and transplant outcomes (LA Baxter-Lowe) – Protocol development – no update

CYTOKINE/CHEMOKINE a. IB14-03a: The prognostic impact of somatic mutations and levels of CXC chemokine

ligands on post hematopoietic cell transplantation (HCT) outcomes in patients with myelodysplastic syndromes (MDS) (W Saber/B Dhakal) Sample typing – no update

OTHER GENES

(Chair: K Fleischhauer) a. IB06-05 Use of high-resolution HLA data from the NMDP for the International

Histocompatibility Working Group in HCT (E Petersdorf) Ongoing – update

Dr. Effie Petersdorf presented this update. She started by mentioning that the 17th International Histocompatibility and Immunogenetics Workshop (IHIWS) will be held from September 6-10, 2017 in Pacific Grove, CA, and that the IHIWS Conference/43rd Annual ASHI Meeting will be from September 11-15, 2017 in San Francisco, CA. As of February 2017, there are 34,998 database submissions to IHIWS Hematopoietic Cell Transplantation working group. Some transplantation working group studies include: 1) Updates on global HLA disparities; 2) Impact of patient-donor race/ethnicity on clinical outcomes after HLA-mismatched unrelated donor HCT; 3) HLA-DPB1 TCE-defined mismatching; 4) Inherited maternal and paternal antigens in cord blood and haploidentical HCT; 5) Importance of HLA expression on transplant outcomes; and 6) KIR studies.

The 2017 Workshop Project will involve HLA-DP as a model for understanding non-coding region variation, using NGS to type 5’ 3’ inclusive of introns and exons; and define the extent of the variation for common HLA-DPB1 alleles.

b. IB09-04 D/R gene polymorphisms of drug metabolisms and innate immune response

post allele matched matched unrelated donor HCT (V Rocha) Manuscript preparation – no update

c. IB09-06/RT09-04 Genetic susceptibility to transplant-related mortality after

unrelated donor stem cell transplant (T Hahn) Ongoing – update

Dr. Theresa Hahn presented this study update. This study is made up of two cohorts; the first being 2609 recipient/donor pairs of 10/10 HLA matched URD BMTs 2000-2008 reported to the CIBMTR with diseases of AML, ALL and MDS and have banked

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samples from the recipient and donor in the Repository; and the 2nd being 923 recipient/donor pairs with same eligibility as cohort 1 except BMTs from 2009-2011, as well as an additional group of 8/8s (but not 10/10s) HLA matched URDs from 2000-2011.

There are multiple hypotheses within the main study: 1) Phenotype Adjudication, 2) GWAS of recipient/donor/recipient & donor matching; 2a) Overall Survival and PFS; 2b) Death due to TRM vs. disease; 2c) Replication of prior Candidate Gene Analyses; 2d) MHC Class II SNPs and OS; 2e) Cause specific TRM (GvHD, Infection, etc.); 2f) Death due to ALL; 2g) Death due to AML/MDS; and 2h) Interaction of variants with RIC/MA and BuCy/CyTBI, 3) ExWAS (Rare variant analysis); 3a) S; 3b( Risk of AML (Case/control), 4) Collaborations with other groups; 4a) GWAS AML/MDS vs. healthy controls (GEMM consortium); 4b) Martin et al. Blood 2016 – replication of candidate gene analyses of chronic GvHD; 4c) Sophie Paczesny (IU) – ST2 SNPs – IBWC study IB15-07; 4d) Shahinaz Gadalla/Sharon Savage (NCI) – CNV analysis, proposal in development. For all publications and presentations, search for DISCOVeRY-BMT study. A web-based application is in the works to use the information made available by all of the studies mentioned.

A question was asked if this contains pharmacogenomic genes. The answer was yes. This project is looking at inherited constitutional genetic variation.

d. IB13-04 Discrepancy analysis of microsatellite loci as a proxy measure for ancestral

differentiation between donors and recipients: correlation between high scores and poorer overall survival in high resolution matched unrelated donor transplantation (J Harvey/C Steward/V Rocha) Sample typing – no update

e. IB14-04 Assessing the similarity of the T cell receptor repertoire in allogeneic

hematopoietic stem cell recipients with the same single human leukocyte mismatches (EH Meyer) Analysis – no update

f. IB14-05 mtDNA haplotypes and unrelated donor transplant outcomes (M Verneris/J

Ross) Sample typing – no update

g. IB15-04 Clinical outcomes among hematopoietic stem cell transplant recipients as a function of socioeconomic status and related transcriptome differences (J Knight, JD Rizzo, S Cole) Protocol development – no update

h. IB15-05 Secondary findings in exome sequencing data (S Savage, S Gadalla) Analysis

– no update

i. IB15-06c Recipient telomere length and outcomes after allogeneic unrelated hematopoietic cell transplant in patients with acute leukemia. (S Gadalla, S Savage) – Manuscript preparation – no update

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j. IB15-07 Functional genetic variants of the ST2 gene in pairs of recipient and donors for risk stratification of GVHD and TRM outcomes (S Paczesny) – no update

k. IB16-01 The role of HLA-E compatibility in the prognosis of acute leukemia patients

undergoing 10/10 HLA matched unrelated HSCT (C Tsamadou, D Fürst, J Mytilineos) Protocol development – no update

l. IB16-03 Role of recipient and donor genetic polymorphisms in interferon lambda 4

(INFL4) on outcomes after unrelated allogeneic cell transplant (S Gadalla) Protocol development – no update

SENSITIZATION/TOLERANCE

(Chair: K Fleischhauer) a. IB11-01 Analysis of the NIMA effect on the outcome of unrelated PBSC/BM

transplantation (G Ehninger/JJ van Rood/A Schmidt) Manuscript preparation – no update

b. IB14-06 Donor-specific anti-HLA antibodies, allele and antigen level HLA mismatches in the outcomes of transplantation of non-malignant diseases with unrelated donors (M Fernandez-Viña/A Woolfrey) Analysis – update

Dr. Ann Woolfrey presented this study update. Donor-specific antibodies (DSA) have been associated with graft failure in multiple studies. For the IB08-02 study led by Dr. John Horan, it was demonstrated that HLA mismatches were associated with higher levels of graft failure following URD HCT for non-malignant disease. For that study DSA was not evaluable for the full cohort, so IB14-06 was designed to test the subset with available pre-HCT patient serum samples. 236 cases were included in the IB14-06 analysis, which were 9/10 matched. These samples were tested at Stanford (by Marcelo Fernandez Vina and Dolly Tyan for PRA, DSA and C1q (complement fixing antibody), and if the DSA or C1q had MFI > 1000, it was considered positive. Preliminary univariate and multivariate analyses were run, with graft failure as the primary outcome. No statistically significant associations were observed, mainly due to such small numbers of cases that were positive for DSA or C1q. A question was asked if the study had looked at various loci. The answer was not yet. This study was based on a non-malignant disease group. Also, it was suggested to check the race of the recipients, the 10/10 against HLA-DPB1, and possibly the MFI cutoff of 1000.

MINOR HISTOCOMPATIBILITY ANTIGENS

No updates.

6. Deferred studies pending accrual/funding a. IB13-06 Role of the complement system in graft-versus-host disease (V Afshar-

Kharghan/J Belmont/C Amos) Pending funding

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b. IB13-07 Impact of donor signal-regulatory protein alpha (SIRPα) polymorphism on outcome of allogeneic hematopoietic stem cell transplantation (allo-HSCT) (A Gassas/J Danska/S Rajakumar) Pending funding

7. Closing remarks (K Hsu)

Dr. Hsu thanked everyone for coming to the meeting and for the good discussion on our topics. She then adjourned the meeting at 3:20 pm.

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WORKING COMMITTEE OVERVIEW PLAN for 2017-2018

STUDIES IN PROGRESS

IB09-04: Donor/recipient gene polymorphisms of drug metabolism and in innate immune response post allele-matched unrelated donor hematopoietic stem cell transplantation (HCT) (V Rocha) - The manuscript is in progress and will be submitted to BBMT by June 2017 (70 statistical hours) The study will be published by December 2017.

IB11-01a: Analysis of the NIMA effect on the outcome of unrelated PBSC/BM transplantation (G Ehninger/J van Rood/A Schmidt) – The manuscript is in progress and will be submitted to BBMT by June 2017 (70 statistical hours) . The study will be published by December 2017.

IB11-01b: Analysis of the IPA effect on the outcome of unrelated PBSC/BM transplantation (G Ehninger/J van Rood/A Schmidt) – Preliminary analysis is completed. The study is currently in data collection and will use 20 statistical hours by June 2017. The goal for June 2018 is to be in data file preparation after using an additional 60 statistical hours. IB12-02c: Prospective assignment of HLA-DPB1 T cell epitope (TCE) group mismatches by functional distance scores compared to the functional TCE assignment algorithm (K Fleischhauer) – The analysis is completed. The June 2017 goal is to have the manuscript submitted using 70 statistical hours to get there. The June 2018 goal is publication. IB13-01: The effect of allele-level HLA-matching on survival after umbilical cord blood transplantation for non-malignant diseases in children (P Veys/M Eapen) – Manuscript preparation is in progress. The June 2017 goal is to have the manuscript submitted using the 50 remaining statistical hours. The June 2018 goal is publication. IB13-04: Discrepancy analysis of microsatellite loci as a proxy measure for ancestral differentiation between donors and recipients: correlation between high scores and poorer overall survival in high resolution matched unrelated donor transplantation (J Harvey/C Steward/V Rocha) - Sample typing is in progress. The status will remain in sample typing with no statistical hours assigned until after June 2017. This study will proceed July 2017 and will use 30 statistical hours to get through data file preparation by October 2017 and start the analysis, using 10 more statistical hours to complete some of the analysis by June 2018. 100 statistical hours will remain on June 2018. IB13-08a: Prediction of acute graft versus host disease post HSCT transplantation using predictive modeling on an ensemble learning framework (R Abdi/S Haneuse/C Lee) - The manuscript is in progress. The June 2017 goal is to submit to Blood, using the remaining 10 statistical hours left for the project. IB13-08b: Analysis of semi-competing risks data from a nested case-control study, applied to acute graft-versus-host disease and death after hematopoietic stem cell transplantation (I Jazic/S Haneuase/S Lee) – this study will use the same data set as IB 13-08a. The June 2017 goal is to commence the analysis. IB13-09: The development of machine learning based classifiers to define the alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation (Y Louzoun) - The

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manuscript is in progress. The June 2017 goal is to submit to BBMT, using the remaining 30 statistical hours. The June 2018 goal is to have it published. IB14-01: Impact of human leukocyte antigen haplotypes on outcomes of allogeneic transplantation for B-cell non-Hodgkin lymphomas (B William/M de Lima/M Fernandez-Vina/B Hill) - Manuscript preparation is in progress. The June 2017 goal is to submit to BBMT, using the remaining 50 statistical hours. The June 2018 goal is to have it published. IB14-02: Structural/Functional models of HLA for data mining of permissive mismatching in allogeneic stem cell transplantation (L Gragert) - The manuscript is in progress. No statistical hours are assigned until after July 2017. The June 2018 goal is to submit to BBMT, using the remaining 70 statistical hours. IB14-03a: The levels of CXC chemokine ligands on post hematopoietic cell transplantation (HCT) outcomes in patients with myelodysplastic syndromes (MDS) (W Saber/B Dhakal) – The study is currently adding more disease cohorts for testing. It currently has 130 total statistical hours assigned, and will use 20 statistical hours to select the samples for typing by April 2017. The study will stay in sample typing until August 2017, and since an existing cohort of samples will be used, 40 statistical hours will be used to reach the June 2018 goal of manuscript preparation. IB14-04: Assessing the similarity of the T cell receptor repertoire in allogeneic hematopoietic stem cell recipients with the same single human leukocyte mismatches (EH Meyer) - Analysis is in progress. This study has 110 statistical hours remaining. 40 statistical hours will be used to reach the goal of manuscript preparation by June 2017. After that the June 2018 goal is to get the manuscript submitted. IB14-05: mtDNA haplotypes and unrelated donor transplant outcomes (M Verneris/L Spector) - Sample typing and data file preparation is in progress. The study has 240 statistical hours remaining. The April 2017 goal is to finish the data file preparation. The sample typing will finish by June 2017. 90 statistical hours will be used to reach this point. The analysis will start in July 2017 and be completed by June 2018 using 80 statistical hours. The June 2018 goal is to reach manuscript preparation with 70 statistical hours remaining. IB14-06: Donor-Specific anti HLA antibodies, allele and antigen level HLA mismatches in the outcomes of transplantation of non-malignant diseases with unrelated donors (M Fernandez-Vina/A Woolfrey) - Analysis is in progress. The April 2017 goal is to start manuscript preparation and to use 20 hours by June 2017. The remaining 50 statistical hours will be used after July 2017 in order to submit the manuscript by June 2018. IB14-08: Development and validation of a clinical unrelated donor selection score (B Shaw/SJ Lee) - Analysis is in progress. The April 2017 goal is to finish the analysis, using 20 statistical hours. The June 2017 goal is to submit the manuscript to Blood. The June 2018 goal is to have it published. IB15-01: The impact of single nucleotide gene polymorphisms (SNP) in the gamma block of the major histocompatibility complex (MHC) on unrelated donor hematopoietic cell transplants (HCT) for hematological malignancies (M Askar/R Sobecks) - Currently this study is in data file preparation. The June 2017 goal is to finish the data file, using 10 statistical hours. Analysis will

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start in July 2017 and will use 80 statistical hours by November 2017 to complete the analysis. The manuscript will be submitted by June 2018, using the remaining 70 statistical hour. IB15-02 Natural killer cell genomics and outcomes after allogeneic transplantation for chronic lymphocytic leukemia (V Bachanova/J Miller/D Weisdorf/S Cooley) - The sample typing is in process. A total of 230 statistical hours are available for this project. 80 statistical hours will be used to finish data file preparation by June 2017. 80 additional statistical hours will be used to finish the analysis by June 2018, at which point this study will move to manuscript preparation. IB15-03 Effect of Killer immunoglobulin like receptors on allogeneic HCT for pediatric acute leukemia (M Verneris/J Miller/S Cooley) – A draft protocol is in process. There are currently 270 statistical hours planned for this project. The April 2017 goal is to finish the protocol, using 20 statistical hours. By August 2017, the study will be in sample typing, using 20 additional statistical hours. By February 2018 data file preparation will be completed using 80 statistical hours. By April 2018, analysis will be completed using 80 more statistical hours. The June 2018 goal is to start manuscript preparation. IB15-04 Clinical outcomes among hematopoietic stem cell transplant recipients as a function of socioeconomic status and related transcriptome differences (J Knight/JD Rizzo/S Cole) - The analysis is in process. The study has 150 statistical hours to be completed. The analysis will be completed by August 2017, using 80 statistical hours. The remaining 70 statistical hours will be used to prepare the manuscript and submit it by June 2018. IB15-05 Secondary Findings in Exome Sequencing Data (S Gadalla/S Savage) - The study is currently in manuscript preparation. The goal is to use the 50 remaining hours by June 2018 is to have the manuscript submitted. IB15-06b: Validation of Telomatch for donor telomere length and outcomes after hematopoietic stem cell transplantation for acute leukemia (S Gadalla/S Savage/DJ Loftus/E Hytopoulos/M Yeager/C Dagnall) - Manuscript preparation is in process. The June 2017 goal is to submit to Blood, using the remaining 10 statistical hours. IB15-07: Functional genetic variants of the ST2 gene in pairs of recipient and donors for risk stratification of GVHD and TRM outcomes (S Paczesny) – Data file preparation is underway. The April 2017 goal is to complete the data file and adding the SNP assignments, using 90 statistical hours. The June 2017 goal is to finish the analysis, using 80 statistical hours. The June 2018 goal is to use the remaining 70 hours to submit the manuscript. IB16-01: The role of HLA-E compatibility in the prognosis of acute leukemia patients undergoing 10/10 HLA matched unrelated HSCT (C Tsamadou/ D Furst/ J Mytilineos). The protocol is under development. The total number of statistical hours to complete the study is 280. The April 2017 goal is to finalize the protocol, using 30 statistical hours. 20 statistical hours will be used to finish sample typing by May 2017. Data file preparation will be completed by December 2017, using 80 statistical hours. Analysis will be completed by April 2018, using another 80 statistical hours. The June 2018 goal is to be in manuscript preparation. IB16-02: Use of HLA structure and function parameters to understand the relationship between HLA disparity and transplant outcomes (LA Baxter-Lowe) - The protocol is under development.

24


This study has 260 total statistical hours assigned to it. 10 statistical hours will be used by June 2017 to be in data file preparation. 100 additional statistical hours will be used to finish data file preparation by August 2017. Analysis will be completed by October 2017, using an additional 80 statistical hours. The June 2018 goal is to start manuscript preparation. IB16-03: Role of recipient and donor genetic polymorphisms in interferon lambda 4 (INFL4) on outcomes after unrelated allogeneic cell transplant (S Gadalla/L Prokunina-Olsson) - Sample typing is underway. The June 2017 goal is to finish the analysis, using 80 statistical hours. An additional 70 statistical hours will be needed to submit the manuscript by June 2018. IB17-01: The impact of HLA-DPB1 levels of expression on clinical outcomes of HCT (M Askar/M Fernandez-Viña) – The protocol will be developed and finalized by January 2018. Samples will be selected and shipped for typing by March 2018. The final testing results will be completed and merged with the data file by June 2018. This goal will take 60 statistical hours. IB17-02: Donor-recipient NK cell determinants associated with survival in JMML after hematopoietic stem cell transplantation (DA Lee/HG Rangarajan) – The protocol will be developed and finalized by December 2017. Samples will be selected and shipped for typing by March 2018. The final testing results will be completed and merged with the data file by June 2018. This goal will take 60 statistical hours. IB17-03: Identification of genomic markers of post hematopoietic cell transplantation (HCT) outcomes in patients with myelofibrosis (MF): A pilot study (W Saber/S Gadalla) – The protocol will be developed and finalized by November 2017. Samples will be selected and shipped for typing by February 2018. The final testing results will be completed and merged with the data file by June 2018. This goal will take 60 statistical hours.

DEFERRED STUDIES

IB13-06 Role of the complement system in graft-versus-host disease (V Afshar-Kharghan /J Belmont/C Amos) – awaiting funding IB13-07 Impact of donor signal-regulatory protein alpha (SIRPα) polymorphism on outcome of allogeneic hematopoietic stem cell transplantation (allo-HCT) (A Gassas/J Danska/S Rajakumar) – awaiting funding

PROPOSALS NOT ACCEPTED

PROP1608-02 Outcomes following umbilical cord blood transplantation for inherited metabolic disorders: do allele-level HLA matching characteristics matter? – Dropped due to overlap with current IB13-01 study.

PROP1610-18 Role of KIR ligand mismatches for transplant outcomes in T-cell replete, non-myeloablative haploidentical transplants, inferring KIR ligand mismatches from HLA-A, -B, and -C locus typing mismatches: a CIBMTR registry analysis – Dropped due to low current numbers and sample availability.

25


PROP1611-36 Impact of KIR ligand mismatches on clinical outcomes following haploidentical stem cell transplantation – Dropped due to low current numbers and sample availability. PROP1611-37 Do hypo-methylating agents following HLA C1/C2 mismatched or HLA/KIR mismatched transplantation improve clinical outcome in acute myeloid leukemia? – Dropped due to limited populations for the study. PROP1611-80 Using donor characteristics and matching at low expression loci to identify optimal 7of 8 HLA matched unrelated adult donors – Dropped due to overlap with IB14-08 study looking at Donor Characteristics and their effects on outcome.

PROP1611-164 Insulin-like growth factor 1 and outcome after myeloablative allogeneic HSCT – Dropped due to lack of feasibility and/or lack of samples. PROP1612-04 KIR and HLA Allotypes and Donor NK Alloreactivity in HLA-Haploidentical Transplantation – Dropped due to low current numbers and sample availability.

OVERSIGHT ASSIGNMENTS FOR WORKING COMMITTEE LEADERSHIP Michael Verneris IB11-01a Analysis of the NIMA effect on the outcome of unrelated

PBSC/BM transplantation IB11-01b Analysis of the IPA effect on the outcome of unrelated

PBSC/BM transplantation IB14-05 mtDNA haplotypes and unrelated donor transplant outcomes

IB14-06 Donor-specific anti-HLA antibodies, allele and antigen level HLA mismatches in the outcomes of transplantation of non-malignant diseases with unrelated donors

IB15-04 Clinical outcomes among hematopoietic stem cell transplant recipients as a function of socioeconomic status and related transcriptome differences IB15-05 Secondary findings in exome sequencing data IB15-07 Functional genetic variants of the ST2 gene in pairs of recipient and donors for risk stratification of GVHD and TRM outcomes IB16-03: Role of recipient and donor genetic polymorphisms in interferon lambda 4 (INFL4) on outcomes after unrelated allogeneic cell transplant IB17-03 Identification of genomic markers of post hematopoietic cell transplantation (HCT) outcomes in patients with myelofibrosis (MF): A

26


pilot study Katharina Fleischhauer IB12-02C Prospective assignment of HLA-DPB1 T cell epitope (TCE)

group mismatches by functional distance scores compared to the functional TCE assignment algorithm

IB13-01 Effect of allele-level HLA-matching after UCB HCT for non-

malignant diseases in children IB13-08a Short and long term survival assessment of post-HSCT

transplantation using predictive modeling on a Bayesian network framework

IB13-08b: Analysis of semi-competing risks data from a nested case-

control study, applied to acute graft-versus-host disease and death after hematopoietic stem cell transplantation (I Jazic/S Haneuase/S Lee)

IB13-09 The development of machine learning based classifiers to

define the alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation

IB14-01 Impact of human leukocyte antigen haplotypes on outcomes of

allogeneic transplantation for B-cell non-Hodgkin lymphomas IB14-02 Structural/Functional models of HLA for data mining of

permissive mismatching in allogeneic hematopoietic stem cell transplantation

IB14-08 Development and validation of a clinical unrelated donor

selection score IB15-01 The impact of single nucleotide gene polymorphisms (SNP) in

the gamma block of the major histocompatibility complex (MHC) on unrelated donor hematopoietic cell transplants (HCT) for hematological malignancies

IB16-02: Use of HLA structure and function parameters to understand the relationship between HLA disparity and transplant outcomes IB17-01 The impact of HLA-DPB1 levels of expression on clinical outcomes of HCT

Katharine Hsu IB15-02 Natural killer cell genomics and outcomes after allogeneic transplantation for chronic lymphocytic leukemia

IB15-03 Killer Immunoglobulin Receptor (KIR) gene content and

pediatric acute leukemia transplant outcomes

27


IB09-04 D/R gene polymorphisms of drug metabolisms and innate immune response post allele matched matched unrelated donor HCT

IB13-04 Discrepancy analysis of microsatellite loci as a proxy measure

for ancestral differentiation between donors and recipients: correlation between high scores and poorer overall survival in high resolution matched unrelated donor transplantation

IB14-03a The levels of CXC chemokine ligands on post hematopoietic

cell transplantation outcomes in patients with myelodysplastic syndromes

IB14-04 Assessing the similarity of the T cell receptor repertoire in

allogeneic hematopoietic stem cell recipients with the same single human leukocyte mismatches

IB16-01: The role of HLA-E compatibility in the prognosis of acute leukemia patients undergoing 10/10 HLA matched unrelated HSCT

IB17-02 Donor-recipient NK cell determinants associated with survival in JMML after hematopoietic stem cell transplantation

28


Accrual Summary for Immunobiology Working Committee

Comprehensive Report Form (CRF) data

CIBMTR HLA-identical

sibling

CIBMTR alternative

related

CIBMTR unrelated (non-US)

CIBMTR unrelated

(US) Number of patients 46882 8985 9388 42216 Number of centers 516 445 229 208 Recipient age at transplant

0-9 6342 (14) 2217 (25) 2157 (23) 6918 (16) 10-19 7675 (16) 1441 (16) 1541 (16) 4985 (12) 20-29 8002 (17) 1276 (14) 1412 (15) 4812 (11) 30-39 8679 (19) 1126 (13) 1563 (17) 5308 (13) 40-49 8088 (17) 1099 (12) 1354 (14) 6434 (15) 50-59 8088 (17) 1823 (20) 1360 (14) 13758 (33) Unknown 8 (N/A) 3 (N/A) 1 (N/A) 1 (N/A) Median (range) 32 (0-82) 26 (0-83) 27 (0-76) 38 (0-83)

Recipient race / ethnicity Caucasian, non-Hispanic 35829 (79) 6297 (73) 7134 (78) 32681 (79) African-American, non-Hispanic 2083 (05) 722 (08) 70 (01) 3130 (08) Asian, non-Hispanic 4533 (10) 904 (11) 1414 (16) 1386 (03) Pacific Islander, non-Hispanic 63 (<1) 16 (<1) 44 (<1) 81 (<1) Native American, non-Hispanic 84 (<1) 39 (<1) 36 (<1) 154 (<1) Hispanic, Caucasian 1074 (02) 336 (04) 290 (03) 2771 (07) Hispanic, African-American 61 (<1) 16 (<1) 15 (<1) 111 (<1) Hispanic, Asian 11 (<1) 3 (<1) 3 (<1) 17 (<1) Hispanic, Pacific islander 5 (<1) 0 0 11 (<1) Hispanic, Native American 17 (<1) 3 (<1) 3 (<1) 34 (<1) Hispanic, race unknown 144 (<1) 27 (<1) 20 (<1) 735 (02) Other 1424 (03) 224 (03) 82 (01) 98 (<1) Unknown 1554 (N/A) 398 (N/A) 277 (N/A) 1007 (N/A)

Recipient sex Male 27400 (58) 5434 (60) 5573 (59) 24705 (59) Female 19482 (42) 3551 (40) 3815 (41) 17511 (41)

Karnofsky score 10-80 12715 (27) 2899 (32) 2529 (27) 12443 (29) 90-100 32541 (69) 5545 (62) 6534 (70) 27734 (66) Missing 1626 (03) 541 (06) 325 (03) 2039 (05)

HLA-A B DRB1 groups - low resolution ≤3/6 0 752 (61) 13 (01) 200 (01) 4/6 0 248 (20) 183 (08) 3890 (10) 5/6 0 122 (10) 590 (26) 8500 (22) 6/6 46882 (100) 108 (09) 1502 (66) 26151 (68) Unknown 0 (N/A) 7755 (N/A) 7100 (N/A) 3475 (N/A)

29



sibling

CIBMTR alternative

related


CIBMTR unrelated

(US) High-resolution HLA matches available out of 8

≤5/8 34 (05) 776 (82) 205 (14) 4928 (15) 6/8 8 (01) 53 (06) 176 (12) 3183 (10) 7/8 31 (04) 72 (08) 397 (27) 6565 (20) 8/8 666 (90) 48 (05) 669 (46) 17559 (54) Unknown 46143 (N/A) 8036 (N/A) 7941 (N/A) 9981 (N/A)

High-resolution HLA typed and audited No 0 0 2 (<1) 184 (01) Yes 10 (100) 9 (100) 509 (>99) 21115 (99) Unknown 46872 (N/A) 8976 (N/A) 8877 (N/A) 20917 (N/A)

Graft type Bone marrow 31627 (67) 5683 (63) 5291 (56) 16609 (39) Peripheral blood 14701 (31) 3137 (35) 2325 (25) 15576 (37) Umbilical cord blood 176 (<1) 39 (<1) 1733 (18) 9746 (23) BM+PBSC 232 (<1) 71 (1) 4 (<1) 8 (<1) BM+UCB 94 (<1) 13 (<1) 2 (<1) 0 PBSC+UCB 3 (<1) 10 (<1) 7 (<1) 198 (<1) Others 49 (<1) 32 (<1) 26 (<1) 79 (<1)

Conditioning regimen Myeloablative 38570 (82) 6678 (74) 7094 (76) 28066 (66) RIC 3247 (07) 684 (08) 1145 (12) 7751 (18) Nonmyeloablative 1888 (04) 988 (11) 531 (06) 4271 (10) Other 3177 (07) 635 (07) 618 (07) 2128 (05)

Donor age at donation To be determined / NA 1514 (03) 381 (04) 1411 (15) 1616 (04) 0-9 5518 (12) 512 (06) 1331 (14) 8789 (21) 10-19 7571 (16) 962 (11) 139 (01) 1007 (02) 20-29 8239 (18) 1723 (19) 1897 (20) 11347 (27) 30-39 8549 (18) 2190 (24) 2465 (26) 10435 (25) 40-49 7798 (17) 1687 (19) 1714 (18) 7078 (17) 50+ 7693 (16) 1530 (17) 431 (05) 1944 (05) Median (range) 31 (-37-85) 35 (-11-81) 32 (0-80) 29 (0-69)

Disease at transplant AML 12295 (26) 2352 (26) 2328 (25) 13379 (32) ALL 7207 (15) 1539 (17) 1960 (21) 6790 (16) Other leukemia 849 (02) 119 (01) 157 (02) 1255 (03) CML 7883 (17) 1030 (11) 1789 (19) 4452 (11) MDS 3995 (09) 798 (09) 978 (10) 6672 (16) Other acute leukemia 366 (01) 107 (01) 121 (01) 435 (01) NHL 3226 (07) 612 (07) 333 (04) 3101 (07) Hodgkins Lymphoma 430 (01) 137 (02) 51 (01) 687 (02) Plasma Cell Disorders, MM 1483 (03) 225 (03) 70 (01) 412 (01)

30



sibling

CIBMTR alternative

related


CIBMTR unrelated

(US) Other malignancies 345 (01) 71 (01) 31 (<1) 92 (<1) Breast cancer 80 (<1) 26 (<1) 2 (<1) 7 (<1) SAA 4441 (09) 572 (06) 494 (05) 1368 (03) Inherited abnormalities erythrocyte diff fxn 3173 (07) 426 (05) 322 (03) 909 (02) SCIDs 657 (01) 731 (08) 363 (04) 1123 (03) Inherited abnormalities of platelets 26 (<1) 9 (<1) 13 (<1) 64 (<1) Inherited disorders of metabolism 270 (01) 165 (02) 244 (03) 940 (02) Histiocytic disorders 119 (<1) 57 (01) 121 (01) 444 (01) Autoimmune disorders 19 (<1) 5 (<1) 4 (<1) 23 (<1) Other 18 (<1) 4 (<1) 7 (<1) 63 (<1)

Disease status at transplant Early 17010 (36) 2172 (24) 2748 (29) 10431 (25) Intermediate 6373 (14) 1506 (17) 2012 (21) 7498 (18) Advanced 5690 (12) 1442 (16) 1249 (13) 7558 (18) Other 17809 (38) 3865 (43) 3379 (36) 16729 (40)

Donor / recipient CMV serostatus Negative / negative 10509 (22) 1897 (21) 2110 (22) 9026 (21) Negative / positive 7185 (15) 1345 (15) 1893 (20) 9636 (23) Positive / negative 4306 (09) 1047 (12) 1073 (11) 3732 (09) Positive / positive 17573 (37) 3093 (34) 2164 (23) 6503 (15) Unknown 7309 (16) 1603 (18) 2148 (23) 13319 (32)

GvHD Prophylaxis Ex vivo T-cell depletion 3415 (07) 1931 (21) 683 (07) 3400 (08) CD34 selection 493 (01) 368 (04) 89 (01) 865 (02) Tacrolimus + MMF ± others 1184 (03) 460 (05) 149 (02) 5934 (14) Tacrolimus + MTX ± others (except MMF) 4292 (09) 374 (04) 560 (06) 11182 (26) Tacrolimus + others (except MTX, MMF) 635 (01) 45 (01) 67 (01) 1737 (04) Tacrolimus alone 334 (01) 64 (01) 29 (<1) 850 (02) CSA + MMF ± others (except Tacrolimus) 1556 (03) 154 (02) 937 (10) 5531 (13) CSA + MTX ± others (except Tacrolimus,

MMF) 21180 (45) 2103 (23) 4908 (52) 7771 (18)

CSA + others (except Tacrolimus, MTX, MMF)

3685 (08) 286 (03) 1026 (11) 2024 (05)

CSA alone 5075 (11) 458 (05) 432 (05) 402 (01) Other GVHD prophylaxis 3136 (07) 323 (04) 68 (01) 567 (01) Missing 1897 (04) 2419 (27) 440 (05) 1953 (05)

Donor / recipient sex match Male / male 8288 (33) 1871 (36) 2706 (38) 12756 (38) Male / female 5505 (22) 889 (17) 1595 (22) 7923 (23) Female / male 6575 (26) 1250 (24) 1515 (21) 7025 (21) Female / female 5028 (20) 1124 (22) 1303 (18) 6160 (18) Unknown 21486 (N/A) 3851 (N/A) 2269 (N/A) 8352 (N/A)

31



sibling

CIBMTR alternative

related


CIBMTR unrelated

(US) Year of transplant

1964-1985 4814 (10) 889 (10) 42 (<1) 11 (<1) 1986 1375 (03) 260 (03) 14 (<1) 18 (<1) 1987 1466 (03) 249 (03) 31 (<1) 34 (<1) 1988 1622 (03) 246 (03) 55 (01) 96 (<1) 1989 1852 (04) 258 (03) 101 (01) 187 (<1) 1990 1953 (04) 321 (04) 142 (02) 302 (01) 1991 1900 (04) 255 (03) 179 (02) 427 (01) 1992 1994 (04) 281 (03) 236 (03) 502 (01) 1993 2006 (04) 288 (03) 242 (03) 605 (01) 1994 1862 (04) 274 (03) 260 (03) 751 (02) 1995 1938 (04) 344 (04) 345 (04) 905 (02) 1996 1994 (04) 339 (04) 436 (05) 1048 (02) 1997 1686 (04) 312 (03) 413 (04) 1131 (03) 1998 1546 (03) 228 (03) 474 (05) 1166 (03) 1999 1389 (03) 216 (02) 471 (05) 1219 (03) 2000 1504 (03) 217 (02) 519 (06) 1288 (03) 2001 1492 (03) 239 (03) 518 (06) 1384 (03) 2002 1437 (03) 203 (02) 481 (05) 1573 (04) 2003 1226 (03) 175 (02) 512 (05) 1728 (04) 2004 1467 (03) 151 (02) 623 (07) 1942 (05) 2005 1504 (03) 184 (02) 597 (06) 2109 (05) 2006 1267 (03) 155 (02) 494 (05) 2426 (06) 2007 757 (02) 95 (01) 332 (04) 2687 (06) 2008 1098 (02) 243 (03) 319 (03) 2323 (06) 2009 928 (02) 165 (02) 270 (03) 2528 (06) 2010 528 (01) 61 (01) 152 (02) 1815 (04) 2011 341 (01) 68 (01) 111 (01) 1455 (03) 2012 354 (01) 85 (01) 176 (02) 1386 (03) 2013 689 (01) 348 (04) 217 (02) 2138 (05) 2014 1005 (02) 456 (05) 234 (02) 2423 (06) 2015 908 (02) 535 (06) 186 (02) 2298 (05) 2016 775 (02) 648 (07) 163 (02) 1892 (04) 2017 205 (<1) 197 (02) 43 (<1) 419 (01)

Follow-up among survivors, months N Eval 22530 3765 4174 15916 Median (range) 94 (0-480) 47 (0-498) 61 (0-316) 70 (0-346)

32


Unrelated Donor HCT Research Sample Inventory - Summary for First Allogeneic Transplants in CRF and TED with biospecimens available through the CIBMTR Repository stratified by availability of paired samples, recipient only samples and donor only samples. Biospecimens include: whole blood, serum/plasma and limited quantities of viable cells and cell lines (collected prior to 2006). Specific inventory queries available upon request through the CIBMTR Immunobiology Research Program

Samples available for recipient and

donor

Samples available for

recipient only

Samples available for donors only

Number of patients 36128 7755 6657 CRF 21258 (59) 4707 (61) 3989 (60) TED 14870 (41) 3049 (39) 2668 (40)

Number of centers 224 198 262 Disease at transplant

AML 12188 (34) 2688 (35) 2209 (33) ALL 5404 (15) 1112 (14) 1102 (17) Other leukemia 1294 (04) 238 (03) 225 (03) CML 3265 (09) 809 (10) 648 (10) MDS 5641 (16) 1270 (16) 944 (14) Other acute leukemia 366 (01) 88 (01) 71 (01) NHL 3450 (10) 655 (08) 516 (08) Hodgkins Lymphoma 770 (02) 126 (02) 111 (02) Plasma Cell Disorders, MM 559 (02) 121 (02) 58 (01) Other malignancies 53 (<1) 12 (<1) 15 (<1) Breast cancer 6 (<1) 1 (<1) 1 (<1) SAA 1170 (03) 252 (03) 247 (04) Inherited abnormalities erythrocyte diff fxn 649 (02) 121 (02) 140 (02) SCIDs 636 (02) 124 (02) 168 (03) Inherited abnormalities of platelets 33 (<1) 7 (<1) 9 (<1) Inherited disorders of metabolism 273 (01) 60 (01) 93 (01) Histiocytic disorders 317 (01) 59 (01) 79 (01) Autoimmune disorders 15 (<1) 4 (<1) 5 (<1) Other 39 (<1) 8 (<1) 16 (<1)

Disease status at transplant Early 10778 (30) 2290 (30) 1828 (27) Intermediate 5401 (15) 1196 (15) 1070 (16) Advanced 4903 (14) 1118 (14) 886 (13) Other 15046 (42) 3151 (41) 2873 (43)

Recipient age at transplant 0-9 3350 (09) 712 (09) 911 (14) 10-19 3483 (10) 712 (09) 775 (12) 20-29 3978 (11) 843 (11) 830 (12) 30-39 4406 (12) 932 (12) 836 (13) 40-49 5781 (16) 1231 (16) 1058 (16) 50-59 15130 (42) 3325 (43) 2247 (34)

33



donor


recipient only


Median (range) 45 (0-84) 46 (0-79) 40 (0-79) Recipient race / ethnicity

Caucasian 30157 (85) 6507 (86) 5076 (83) African-American 1651 (05) 365 (05) 277 (05) Asian 774 (02) 178 (02) 290 (05) Pacific islander 51 (<1) 5 (<1) 9 (<1) Native American 111 (<1) 24 (<1) 17 (<1) Hispanic 2507 (07) 487 (06) 413 (07) Other 44 (<1) 21 (<1) 19 (<1) Unknown 833 (N/A) 168 (N/A) 556 (N/A)

Recipient sex Male 21073 (58) 4637 (60) 3955 (59) Female 15055 (42) 3118 (40) 2702 (41)

Karnofsky score 10-80 11639 (32) 2673 (34) 1940 (29) 90-100 23067 (64) 4818 (62) 4250 (64) Missing 1422 (04) 264 (03) 467 (07)

HLA-A B DRB1 groups - low resolution ≤ 3/6 21 (<1) 4 (<1) 1 (<1) 4/6 207 (01) 64 (01) 28 (<1) 5/6 5274 (15) 1154 (15) 971 (15) 6/6 30212 (85) 6236 (84) 5296 (84) Unknown 414 (N/A) 297 (N/A) 361 (N/A)

High-resolution HLA matches available out of 8 ≤ 5/8 811 (02) 59 (01) 41 (01) 6/8 1602 (05) 103 (02) 137 (03) 7/8 7211 (21) 989 (19) 1024 (24) 8/8 24802 (72) 4050 (78) 3083 (72) Unknown 1702 (N/A) 2554 (N/A) 2372 (N/A)

HLA-DPB1 Match Double allele mismatch 7965 (30) 443 (26) 304 (29) Single allele mismatch 14167 (54) 897 (52) 557 (53) Full allele matched 4021 (15) 392 (23) 191 (18) Unknown 9975 (N/A) 6023 (N/A) 5605 (N/A)

High resolution release score No 281 (01) 8 (02) 3 (01) Yes 25167 (99) 339 (98) 211 (99) Unknown 10680 (N/A) 7408 (N/A) 6443 (N/A)

KIR typing available No 28852 (80) 7661 (99) 6636 (>99) Yes 7276 (20) 94 (01) 21 (<1)

Graft type

34



donor


recipient only


Bone marrow 14198 (39) 3321 (43) 3063 (46) Peripheral blood 21912 (61) 4364 (56) 3583 (54) BM+PBSC 7 (<1) 1 (<1) 5 (<1) PBSC+UCB 11 (<1) 69 (1) 6 (<1)

Conditioning regimen Myeloablative 19309 (53) 4168 (54) 3615 (54) RIC 4969 (14) 1099 (14) 908 (14) Nonmyeloablative 1887 (05) 350 (05) 310 (05) To be determined 9963 (28) 2138 (28) 1824 (27)

Donor age at donation To be determined / N/A 3 (<1) 98 (01) 0 0-9 15 (<1) 52 (01) 5 (<1) 10-19 984 (03) 200 (03) 146 (02) 20-29 15369 (43) 3285 (42) 2514 (38) 30-39 10622 (29) 2331 (30) 2080 (31) 40-49 6976 (19) 1380 (18) 1435 (22) 50+ 2159 (06) 409 (05) 477 (07) Median (range) 31 (0-73) 31 (0-73) 33 (3-66)

Donor / recipient CMV serostatus Negative / negative 8172 (23) 1875 (24) 1376 (21) Negative / positive 8742 (24) 1905 (25) 1609 (24) Positive / negative 4372 (12) 978 (13) 854 (13) Positive / positive 8725 (24) 1869 (24) 1624 (24) Unknown 6117 (17) 1128 (15) 1194 (18)

GvHD Prophylaxis Ex vivo T-cell depletion 2116 (06) 561 (07) 508 (08) CD34 selection 678 (02) 200 (03) 127 (02) Tacrolimus + MMF ± others 4508 (12) 841 (11) 597 (09) Tacrolimus + MTX ± others (except MMF) 14786 (41) 2998 (39) 1964 (30) Tacrolimus + others (except MTX, MMF) 1860 (05) 411 (05) 260 (04) Tacrolimus alone 826 (02) 199 (03) 116 (02) CSA + MMF ± others (except Tacrolimus) 2445 (07) 443 (06) 567 (09) CSA + MTX ± others (except Tacrolimus,

MMF) 6340 (18) 1530 (20) 1838 (28)


689 (02) 134 (02) 192 (03)

CSA alone 282 (01) 47 (01) 128 (02) Other GVHD prophylaxis 507 (01) 109 (01) 76 (01) Missing 1091 (03) 282 (04) 284 (04)

Donor / recipient sex match Male / male 14851 (41) 3287 (42) 2666 (40) Male / female 8997 (25) 1938 (25) 1524 (23)

35



donor


recipient only


Female / male 6220 (17) 1339 (17) 1289 (19) Female / female 6057 (17) 1171 (15) 1178 (18) Unknown 3 (N/A) 20 (N/A) 0 (N/A)

Year of transplant 1986-1990 409 (01) 35 (<1) 49 (01) 1991-1995 1959 (05) 484 (06) 452 (07) 1996-2000 3266 (09) 1209 (16) 733 (11) 2001-2005 4936 (14) 1029 (13) 1411 (21) 2006-2010 9081 (25) 1312 (17) 1579 (24) 2011-2015 12918 (36) 2506 (32) 1884 (28) 2016-2017 3559 (10) 1180 (15) 549 (08)

Follow-up among survivors, months N Eval 15070 3108 2477 Median (range) 49 (0-344) 38 (3-312) 48 (1-313)

36


Unrelated Cord Blood Transplant Research Sample Inventory - Summary for First Allogeneic Transplants in CRF and TED with biospecimens available through the CIBMTR Repository stratified by availability of paired, recipient only and cord blood only samples. Biospecimens include: whole blood, serum/plasma and limited quantities of viable cells and cell lines (collected prior to 2006-recipient only). Specific inventory queries available upon request through the CIBMTR Immunobiology Research Program

Samples available for recipient

and donor


recipient only


donor only Number of patients 4796 607 1263

CRF 3664 (76) 471 (78) 898 (71) TED 1132 (24) 136 (22) 365 (29)


AML 1772 (37) 213 (35) 402 (32) ALL 983 (20) 116 (19) 274 (22) Other leukemia 86 (02) 10 (02) 22 (02) CML 111 (02) 12 (02) 39 (03) MDS 457 (10) 56 (09) 115 (09) Other acute leukemia 76 (02) 7 (01) 19 (02) NHL 330 (07) 34 (06) 80 (06) Hodgkins Lymphoma 89 (02) 10 (02) 16 (01) Plasma Cell Disorders, MM 25 (01) 6 (01) 2 (<1) Other malignancies 9 (<1) 0 0 SAA 88 (02) 19 (03) 30 (02) Inherited abnormalities erythrocyte diff fxn 139 (03) 21 (03) 43 (03) SCIDs 219 (05) 36 (06) 85 (07) Inherited abnormalities of platelets 13 (<1) 3 (<1) 6 (<1) Inherited disorders of metabolism 282 (06) 48 (08) 94 (07) Histiocytic disorders 97 (02) 14 (02) 28 (02) Autoimmune disorders 9 (<1) 0 4 (<1) Other 11 (<1) 2 (<1) 4 (<1)



37



and donor


recipient only


donor only Median (range) 26 (0-81) 16 (0-75) 18 (0-78)

Recipient race / ethnicity Caucasian 2686 (59) 351 (61) 669 (58) African-American 686 (15) 82 (14) 175 (15) Asian 277 (06) 40 (07) 97 (08) Pacific islander 19 (<1) 2 (<1) 12 (01) Native American 27 (01) 3 (01) 10 (01) Hispanic 880 (19) 96 (17) 196 (17) Other 0 0 1 (<1) Unknown 221 (N/A) 33 (N/A) 103 (N/A)



HLA-A B DRB1 groups - low resolution ≤ 3/6 25 (01) 3 (01) 5 (<1) 4/6 1962 (41) 253 (43) 504 (40) 5/6 2157 (45) 256 (43) 579 (46) 6/6 623 (13) 81 (14) 166 (13) Unknown 29 (N/A) 14 (N/A) 9 (N/A)

High-resolution HLA matches available out of 8 ≤ 5/8 2385 (56) 250 (55) 572 (56) 6/8 1028 (24) 109 (24) 241 (24) 7/8 574 (13) 65 (14) 141 (14) 8/8 298 (7) 28 (6) 67 (7) Unknown 511 (N/A) 155 (N/A) 242 (N/A)

HLA-DPB1 Match Double allele mismatch 627 (39) 40 (40) 28 (36) Single allele mismatch 836 (52) 49 (49) 41 (53) Full allele matched 143 (09) 11 (11) 8 (10) Unknown 3190 (N/A) 507 (N/A) 1186 (N/A)

High resolution release score No 24 (02) 0 1 (14) Yes 1470 (98) 71 (100) 6 (86) Unknown 3302 (N/A) 536 (N/A) 1256 (N/A)

KIR typing available No 3503 (73) 593 (98) 1256 (99) Yes 1293 (27) 14 (02) 7 (01)

Cord blood number of units

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and donor


recipient only


donor only Single units 3271 (68) 0 971 (77) Double units 1522 (32) 0 292 (23) Triple units 3 (<1) 0 0 Unknown 0 (N/A) 607 (N/A) 0 (N/A)

Graft type Umbilical cord blood 4608 (96) 538 (89) 1206 (95) BM+UCB 1 (<1) 0 1 (<1) PBSC+UCB 187 (04) 69 (11) 56 (04)


Donor age at donation To be determined / N/A 16 (<1) 25 (04) 13 (01) 0-9 4489 (94) 502 (83) 1168 (92) 10-19 234 (05) 44 (07) 62 (05) 20-29 12 (<1) 7 (01) 4 (<1) 30-39 22 (<1) 12 (02) 7 (01) 40-49 10 (<1) 9 (01) 5 (<1) 50+ 13 (<1) 8 (01) 4 (<1) Median (range) 3 (0-68) 4 (0-73) 3 (0-67)


GvHD prophylaxis Ex vivo T-cell depletion 22 (<1) 8 (01) 5 (<1) CD34 selection 184 (04) 57 (09) 53 (04) Tacrolimus + MMF ± others 1312 (27) 144 (24) 230 (18) Tacrolimus + MTX ± others (except MMF) 167 (03) 31 (05) 60 (05) Tacrolimus + others (except MTX, MMF) 223 (05) 29 (05) 57 (05) Tacrolimus alone 98 (02) 15 (02) 25 (02) CSA + MMF ± others (except Tacrolimus) 2095 (44) 200 (33) 559 (44) CSA + MTX ± others (except Tacrolimus,

MMF) 86 (02) 19 (03) 29 (02)


290 (06) 68 (11) 132 (10)

CSA alone 40 (01) 7 (01) 29 (02) Other GVHD prophylaxis 119 (02) 4 (01) 14 (01)

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and donor


recipient only


donor only Missing 160 (03) 25 (04) 70 (06)

Donor / recipient sex match Male / Male 1328 (28) 188 (31) 378 (30) Male / Female 1055 (22) 127 (21) 274 (22) Female / Male 1266 (27) 173 (29) 351 (28) Female / Female 1060 (23) 113 (19) 239 (19) Unknown 87 (N/A) 6 (N/A) 21 (N/A)

Year of transplant 1996-2000 0 3 (<1) 4 (<1) 2001-2005 112 (02) 100 (16) 24 (02) 2006-2010 1737 (36) 123 (20) 472 (37) 2011-2015 2451 (51) 260 (43) 657 (52) 2016-2017 496 (10) 121 (20) 106 (08)


40


Related Donor HCT Research Sample Inventory - Summary for First Allogeneic Transplants in CRF and TED with biospecimens available through the CIBMTR Repository stratified by availability of paired, recipient only and donor only samples. Biospecimens include: whole blood, serum/plasma and limited quantities of viable cells and cell lines (collected prior to 2006). Specific inventory queries available upon request through the CIBMTR Immunobiology Research Program


donor


recipient only


donor only Number of patients 4913 726 372

CRF 1800 (37) 187 (26) 122 (33) TED 3113 (63) 536 (74) 250 (67)


AML 1625 (33) 238 (33) 111 (30) ALL 746 (15) 127 (17) 68 (18) Other leukemia 126 (03) 23 (03) 10 (03) CML 183 (04) 20 (03) 10 (03) MDS 798 (16) 112 (15) 53 (14) Other acute leukemia 71 (01) 15 (02) 3 (01) NHL 549 (11) 86 (12) 41 (11) Hodgkins Lymphoma 107 (02) 15 (02) 16 (04) Plasma Cell Disorders, MM 118 (02) 24 (03) 12 (03) Other malignancies 10 (<1) 0 0 Breast cancer 1 (<1) 0 0 SAA 210 (04) 21 (03) 13 (03) Inherited abnormalities erythrocyte diff fxn 234 (05) 38 (05) 24 (06) SCIDs 86 (02) 7 (01) 6 (02) Inherited abnormalities of platelets 8 (<1) 0 0 Inherited disorders of metabolism 6 (<1) 0 1 (<1) Histiocytic disorders 25 (01) 0 2 (01) Autoimmune disorders 3 (<1) 0 0 Other 7 (<1) 0 2 (01)



41



donor


recipient only



Recipient race/ethnicity Caucasian 3170 (67) 401 (58) 225 (63) African-American 586 (12) 86 (12) 37 (10) Asian 230 (05) 53 (08) 29 (08) Pacific islander 18 (<1) 3 (<1) 0 Native American 15 (<1) 3 (<1) 0 Hispanic 737 (15) 145 (21) 64 (18) Unknown 157 (N/A) 35 (N/A) 17 (N/A)



Donor type from retrieval HLA-identical sibling 3747 (76) 553 (76) 283 (76) Identical twin 25 (01) 4 (01) 3 (01) Other relative 1 (<1) 0 1 (<1) HLA-matched other relative 122 (02) 23 (03) 13 (03) HLA-mismatched relative 1017 (21) 145 (20) 72 (19) Unknown 1 (N/A) 1 (N/A) 0 (N/A)

Graft type Bone marrow 1346 (27) 155 (21) 99 (27) Peripheral blood 3544 (72) 567 (78) 268 (72) BM+PBSC 4 (<1) 2 (<1) 0 BM+UCB 16 (<1) 2 (<1) 2 (01) PBSC+UCB 3 (<1) 0 3 (01)


Donor age at donation To be determined / N/A 3113 (63) 544 (75) 250 (67) 0-9 128 (3) 8 (1) 6 (2) 10-19 148 (3) 13 (2) 14 (4) 20-29 224 (5) 17 (2) 10 (3) 30-39 226 (5) 20 (3) 14 (4) 40-49 304 (6) 29 (4) 23 (6) 50+ 770 (16) 95 (13) 55 (15)

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donor


recipient only




GvHD prophylaxis No GVHD prophylaxis 50 (01) 5 (01) 5 (01) Ex-vivo T-cell depletion 60 (01) 5 (01) 8 (02) CD34 selection 63 (01) 23 (03) 6 (02) Post-CY + other(s) 707 (14) 106 (15) 41 (11) TAC + MMF ± other(s) (except post-CY) 647 (13) 71 (10) 42 (11) TAC + MTX ± other(s) (except MMF, post-

CY) 2128 (43) 272 (37) 184 (49)

TAC + other(s) (except MMF, MTX, post-CY) 452 (09) 156 (21) 33 (09) TAC alone 45 (01) 8 (01) 3 (01) CSA + MMF ± other(s) (except post-CY) 134 (03) 12 (02) 5 (01) CSA + MTX ± other(s) (except MMF, post-

CY) 390 (08) 31 (04) 25 (07)

CSA + other(s) (except MMF, MTX, post-CY) 46 (01) 10 (01) 1 (<1) CSA alone 31 (01) 4 (01) 3 (01) Other(s) 90 (02) 13 (02) 5 (01) Missing 70 (01) 10 (01) 11 (03)

Donor / recipient sex match Male / male 1581 (32) 265 (37) 123 (33) Male / female 1086 (22) 127 (17) 81 (22) Female / male 1295 (26) 164 (23) 85 (23) Female / female 946 (19) 170 (23) 83 (22) Unknown 5 (N/A) 0 (N/A) 0 (N/A)

Year of transplant 2006-2010 517 (11) 63 (09) 38 (10) 2011-2015 2983 (61) 420 (58) 248 (67) 2016-2017 1413 (29) 243 (33) 86 (23)


43


Proposal 1710-09

Title: Clonal Mosaicism and HCT Outcomes in Patients with Acute Leukemia and Myelodysplastic Syndromes

Lara Sucheston-Campbell: College of Pharmacy, The Ohio State University Theresa Hahn: Department of Medicine, Roswell Park Cancer Institute Shahinaz Gadalla: Clinical Genetics Branch, National Cancer Institute

Collaborators: Sharon Savage: National Cancer Institute Meredith Yeager: National Cancer Institute Weiyin Zhou: National Cancer Institute

Specific aims: • Characterize clonal mosaic alterations in a cohort of patients with acute leukemia and MDS,

overall, and by disease subtype• Compare the pattern of detected somatic mosaicism in patients with leukemia/MDS by their

disease status at HCT (early (CR1/CR2) versus late (not in CR1 or CR2))• Evaluate the effect of detected mosaic chromosomal alterations on patients’ risk of relapse and

GvHD• Determine the effect of such alterations on survival outcomes 1 year after HCT, including:

overall survival, progression free survival, transplant related mortality (including death due toGvHD) and death due to disease.

Scientific justification: Recent bioinformatics tool development make it possible to use genome-wide single nucleotide polymorphism (SNP) data to detect large mosaic chromosomal alterations in DNA samples. This platform can detect genomic gains, loss, or copy neutral loss of heterozygosity (CN-LOH)(1,2). This allows for more precise mapping of chromosomal alterations and determination of the degree to which it occurs. This work has improved understanding of the degree to which mosaicism occurs in blood or buccal cell DNA from germline samples and led to the development of new hypotheses in their contribution to human disease. For example, large population studies of constitutional DNA have shown that such chromosomal alterations increase with age, are more common in men, and in individuals of European ancestry(1,2). Mosaic CN-LOH alterations are most common (48.2% of all changes), followed by copy gains (34.8%), copy losses (15.1%), and complex events (6%)(3). While those events are rare in the general population (<1%), they are significantly higher in patients with hematological malignancies (16.5%)(1). Classic cytogenetic analysis in the hematological malignancies significantly improved the diagnosis and prognosis for patients and is key in determining both conventional therapy and HCT. Studies comparing clinical cytogenetic testing and SNP-array analyses showed a high detection power for the arrays (approximately 80% in most cases), and highlighted that cytogenetic analyses miss important changes, particularly CN-LOH(4). For example, a study of 86 AML patients found that 40% of the patients carried alterations that were not detected with cytogenetics(5). A recent study of 104 patients with MDS comparing conventional karyotyping to SNP-array analysis, showed a high detection rate in the array-based method (55 abnormal cases vs. 35 in karyotying). All detected abnormalities by karyotyping were also detected by SNP-array except for subclones in two cases and balanced translocations in three cases.

44


Of note, patients who failed karyotyping testing because of insufficient metaphases, were successfully evaluated by SNP-array(6). In the TOAA study (CIBMTR proposal IB10-01), SNP array analysis of germline DNA in patients with severe aplastic anemia (SAA) showed a high frequency (11%) of somatic chromosomal alterations. The majority were CN-LOH affecting the HLA locus on chromosome 6 (Gadalla et., unpublished data). A similar finding was reported in a smaller study, mostly of newly diagnosed, Japanese SAA patients(7). Preliminary analysis in TOAA shows an association between such alterations and patient post-HCT outcomes. In hematological malignancies, CN-LOH involving oncogenes or tumor suppressor genes has been identified as a mechanism of tumor initiation and progression (reviewed in(8). Changes involving the HLA region have also been described in case reports(9,10). It has been suggested that somatic alterations affecting HLA in leukemia patients provides selective advantage to the tumor cells through escaping immune surveillance(11). The frequency of CN-LOH involving HLA in patients in remission after leukemia treatment, and whether such events have an impact on patient post-HCT outcome are not fully understood. CN-LOH in the HLA region associated with relapse in studies of haploidentical HCT(12) but evidence is not clear in HLA-matched HCT. Study population: Analyses of mosaicism frequency by disease and correlation with survival will be done with data from an existing genome-wide association study (GWAS) called DISCOVeRY-BMT(13). This GWAS analyzed the association of recipient survival following an unrelated donor (URD) BMT with non-HLA genetic variation in recipient and donor genomes(13,14). All patients included in DISCOVeRY-BMT provided informed consent for inclusion in the Center for International Blood and Marrow Transplant Research (CIBMTR) registry. The National Marrow Donor Program (NMDP) and Roswell Park Cancer Institute Institutional Review Boards approved the study protocol. Paired donor and recipient biospecimens and corresponding clinical data were obtained from the CIBMTR biorepository and database. DISCOVeRY-BMT consists of two independent cohorts of patients with AML, ALL, or MDS. Cohort 1 included 2,609 10/10 HLA-matched URD-BMT recipients with AML, ALL or MDS from 2000-2008 and Cohort 2 included 923 10/10 HLA-matched URD-BMT recipients with AML, ALL or MDS from 2009-2011 as well as 8/8 (but not 10/10) HLA-matched URD-BMT recipients from 2000-2011. Patients were excluded if they received T-cell depleted or cord blood grafts. Causes of death were adjudicated by an expert panel and the primary cause of death was defined as Disease-Related Mortality (DRM) or Transplant-Related Mortality (TRM). Genotyping and Imputation All samples were genotyped using the Illumina Human OmniExpress BeadChip and the Illumina HumanExome BeadChip (University of Southern California Genomics Facility). Samples were assigned to plates to ensure the even distribution of patient characteristics and potential confounding variables using Optimal Sample Assignment Tool (OSAT), an R/Bioconductor software package. Quality assessment was done within each self-reported race group and included sample and SNP call rates, sample relatedness and sex. Samples that did not pass quality control were excluded. Genotyped SNPs were removed if the call rate was <98%, if there was deviation from Hardy-Weinberg equilibrium proportions or if discordance between duplicate samples was >2%. Detection of clonal mosaic events We will use the mosaic alteration detection (MAD) algorithm implemented in R Genomic Alteration Detection Analysis (R-GADA) software to calculate B-allele frequency (BAF), and Log R ratio (LRR) from the GWAS data. BAF is defined as the ratio of probe values relative to the locations of the estimated

45


genotype-specific clusters. LRR calculates the log base 2 of the ratio of observed total signal intensities to expected signal intensities for a SNP. BAF is a measure of allelic imbalance, used to detect copy-neutral loss of heterozygosity (CN-LOH). While LRR is used to assess copy number variation, LRR >0 indicate copy gain, and <0 indicate loss. LRR of zero with abnormal heterozygous BAF indicates CN-LOH(15,16). Events >2 Mb in size have been shown to be reduce false discovery rate, however even for events < 2Mb calling of mosaic events is far superior to that of CNV detection methods and thus at first pass we consider only those >2 Mb(1). We will also explore events of smaller size as well. Study design:

• Characterize clonal mosaic alterations in a cohort of patients with acute leukemia and MDS, overall, and by disease subtype.

For the entire study population, we will determine the count and frequency of chromosomal abnormalities by event type (gain, loss, copy number – loss of heterozygosity and mixed) and location (chromosome, telomeric p, telomeric q, interstitial, spanning centromere, complex). In addition, we will assess the distribution and frequency of specific clonal mosaic events, including if mosaic events appear multiple times in the leukemia patients. The clonal mosaic alterations identified will be further characterized by disease subtype in both cohorts, including: B and T-cell ALL, treatment related AML and all de novo AML cytogenetic subtypes available (abnormal, normal, any trisomy, any monosomy, core binding factor (CBF), deletion/loss of part of chromosomes 5 and/or 7, MLL, any translocation, 3 or more complex changes, t(8;21)). We recognize that subtypes have small numbers in both cohorts and thus reduced probability of seeing mosaic alterations, e.g., CBF represents the smallest N and includes 67 and 32 patients in cohorts 1 and 2, respectively. However, we will report mosaic alterations for all of the above cytogenetic and subtype groups.

• To compare the pattern of detected somatic mosaicism in patients with leukemia/MDS by

disease, disease stage and disease status at HCT (CR1, vs. CR2)

To determine the relationship between detectable mosaic autosomal abnormalities and DISCOVeRY-BMT patients. We will regress the presence of detectable clonal mosaicism on disease, disease stage and disease status, adjusting for important demographic and clinical factors such as, age, donor age, sex combination of donor-recipient pair and DNA source in a logistic model. Approximately 2/3 of DISCOVeRY-BMT AML patients are in CR1/CR2/CR3. For MDS most patients are considered to have measurable disease untreated with chemotherapy.

• Evaluate the effect of detected mosaic chromosomal alterations on patient risk of relapse, and

GvHD

We will use cumulative incidence estimator to calculate the cumulative incidence of relapse at 1 and 2 years and GvHD (acute GvHD at 100 days, and chronic GvHD at 1 and 2 years) in those with alterations (overall, and focusing on frequently observed) and those without. For multivariable analysis, we will use Cox proportional hazard models to calculate hazard ratios (HR) and 95% confidence intervals (CI). We will select variables in the model using a stepwise forward-backward procedure with a P threshold of .05 for both entry and retention in the model. The proportional hazard assumption will be tested for all variables included in the model, and stratification was used if the proportionality assumption was not met.

46


• To determine the effect of donor and recipient alterations on survival outcomes 1 year after

HCT, including: overall survival, progression free survival, transplant related mortality (including death due to GvHD) and death due to disease). Clinical covariates for inclusion in survival models were selected using bidirectional stepwise Cox proportional

hazard models of OS, PFS, TRM and DRM in R statistical software (previously described in(17)). Genetic models for OS will be adjusted for recipient age, disease status (early/intermediate or advanced), and graft source (blood or marrow); PFS and DRM genetic models will be adjusted for recipient age and disease status; TRM genetic models will be adjusted for recipient age, graft source and body mass index (underweight/normal, overweight, or obese). For GvHD-related mortality analyses will be adjusted for disease type (ALL, MDS), graft type, body mass index (obese), age and donor age. To combine data from DISCOVeRY-BMT Cohorts 1 and 2, effect size estimates and standard errors for each analyses will be analyzed using METAL software(18). For all analyses, we will report the meta hazard ratios (HR), confidence intervals (CI), and P-values under a fixed effects model. Likelihood ratio test as implemented in R statistical software will be used to analyze the hazard of recipient death attributable to clonal mosaicism (yes/no) in recipients. We will treat recurrent/frequent detectable events (>20) across recipients as presence/absence and will correlate these with recipient survival outcomes.

Outcomes to be analyzed:

• Acute GvHD at 100 days (data to be requested) • Chronic GvHD at 1 and 2 years (data to be requested) • Relapse at 1 and 2 years (data to be requested) • Overall survival at 1 year (data currently available in DISCOVeRY-BMT) • Progression free survival, and transplant related mortality at 1 year (data currently available in

DISCOVeRY-BMT) • GvHD- and relapse-related mortalities at 1 year (data currently available in DISCOVeRY-BMT)

Power Calculation Minimal detectable hazard ratios for associations for various mosaicism frequencies are shown in Table1 where Proportion of Events reflects number of survival outcomes of interest or acute/chronic GvHD over total number of individuals. Freq. reflect the frequency of chromosomal abnormalities with the Minimum Detectable Hazard Ratio showing the HR for combination of events and chromosomal abnormalities. The hazard ratios are calculated assuming 80% power.

Proportion of Events

Minimum Detectable Hazard Ratio

Freq=0.20 Freq=0.10 Freq=0.025

47


References: 1. Jacobs KB, Yeager M, Zhou W, Wacholder S, Wang Z, Rodriguez-Santiago B, et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nature genetics 2012;44(6):651-8. doi 10.1038/ng.2270. 2. Laurie CC, Laurie CA, Rice K, Doheny KF, Zelnick LR, McHugh CP, et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet 2012;44(6):642-50. doi ng.2271 [pii] 10.1038/ng.2271. 3. Machiela MJ, Zhou W, Sampson JN, Dean MC, Jacobs KB, Black A, et al. Characterization of large structural genetic mosaicism in human autosomes. Am J Hum Genet 2015;96(3):487-97. doi 10.1016/j.ajhg.2015.01.011. 4. Vosberg S, Herold T, Hartmann L, Neumann M, Opatz S, Metzeler KH, et al. Close correlation of copy number aberrations detected by next-generation sequencing with results from routine cytogenetics in acute myeloid leukemia. Genes, Chromosomes & Cancer 2016;55(7):553-67. doi 10.1002/gcc.22359. 5. Walter MJ, Payton JE, Ries RE, Shannon WD, Deshmukh H, Zhao Y, et al. Acquired copy number alterations in adult acute myeloid leukemia genomes. Proceedings of the National Academy of Sciences of the United States of America 2009;106(31):12950-5. doi 10.1073/pnas.0903091106. 6. Stevens-Kroef MJ, Olde Weghuis D, ElIdrissi-Zaynoun N, van der Reijden B, Cremers EMP, Alhan C, et al. Genomic array as compared to karyotyping in myelodysplastic syndromes in a prospective clinical trial. Genes, Chromosomes & Cancer 2017;56(7):524-34. doi 10.1002/gcc.22455. 7. Katagiri T, Sato-Otsubo A, Kashiwase K, Morishima S, Sato Y, Mori Y, et al. Frequent loss of HLA alleles associated with copy number-neutral 6pLOH in acquired aplastic anemia. Blood 2011;118(25):6601-9. doi 10.1182/blood-2011-07-365189. 8. O'Keefe C, McDevitt MA, Maciejewski JP. Copy neutral loss of heterozygosity: a novel chromosomal lesion in myeloid malignancies. Blood 2010;115(14):2731-9. doi 10.1182/blood-2009-10-201848. 9. Sayer DC, Smith LK, Krueger R, Chrisitansen FT. DNA sequencing-based HLA typing detects a B-cell ALL blast-specific mutation in HLA-A(*)2402 resulting in loss of HLA allele expression. Leukemia 2004;18(1):174-6. doi 10.1038/sj.leu.2403150. 10. Pereira S, Vayntrub T, Hiraki DD, Cherry AM, Arai S, Dvorak CC, et al. Short tandem repeat and human leukocyte antigen mutations or losses confound engraftment and typing analysis in hematopoietic stem cell transplants. Hum Immunol 2011;72(6):503-9. doi 10.1016/j.humimm.2011.03.003. 11. Smith AG, Fan W, Regen L, Warnock S, Sprague M, Williams R, et al. Somatic mutations in the HLA genes of patients with hematological malignancy. Tissue Antigens 2012;79(5):359-66. doi 10.1111/j.1399-0039.2012.01868.x. 12. Vago L, Perna SK, Zanussi M, Mazzi B, Barlassina C, Stanghellini MT, et al. Loss of mismatched HLA in leukemia after stem-cell transplantation. N Eng J Med 2009;361(5):478-88. doi 10.1056/NEJMoa0811036. 13. Hahn T, Sucheston-Campbell LE, Preus L, Zhu X, Hansen JA, Martin PJ, et al. Establishment of Definitions and Review Process for Consistent Adjudication of Cause-specific Mortality after Allogeneic Unrelated-donor Hematopoietic Cell Transplantation. Biol Blood Marrow Transplant 2015;21(9):1679-86. doi 10.1016/j.bbmt.2015.05.019.

0.1 2.04 2.67 3.70

0.2 1.62 2.04 2.50

0.3 1.49 1.74 2.10

0.4 1.37 1.55 1.90

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14. Martin PJ, Fan W, Storer BE, Levine DM, Zhao LP, Warren EH, et al. Replication of associations between genetic polymorphisms and chronic graft-versus-host disease. Blood 2016;128(20):2450-6. doi 10.1182/blood-2016-07-728063. 15. Peiffer DA, Le JM, Steemers FJ, Chang W, Jenniges T, Garcia F, et al. High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res 2006;16(9):1136-48. doi 10.1101/gr.5402306. 16. Staaf J, Vallon-Christersson J, Lindgren D, Juliusson G, Rosenquist R, Hoglund M, et al. Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios. BMC Bioinformatics 2008;9:409. doi 10.1186/1471-2105-9-409. 17. Karaesmen E, Rizvi AA, Preus L, McCarthy PL, Pasquini MC, Onel K, et al. Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant. Blood 2017. doi 10.1182/blood-2017-05-784637. 18. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010;26(17):2190-1. doi 10.1093/bioinformatics/btq340.

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Table 1. Characteristics of Patients for ALL/AML/MDS diseases in DISCOVeRY-BMT cohort ALL AML MDS Variable N (%) N (%) N (%) Number of recipients 777 2107 648 Patient-related Recipient age at transplant

0-9 103 (13) 80 ( 4) 32 ( 5) 10-19 152 (20) 139 ( 7) 32 ( 5) 20-29 162 (21) 255 (12) 39 ( 6) 30-39 135 (17) 223 (11) 58 ( 9) 40-49 93 (12) 439 (21) 104 (16) 50-59 100 (13) 560 (27) 218 (34) 60+ 32 ( 4) 411 (20) 165 (25) Median (range) 28 (0-68) 48 (1-78) 53 (0-74)

Recipient race / ethnicity African-American, non-Hispanic 28 ( 4) 37 ( 2) 13 ( 2) Asian, non-Hispanic 12 ( 2) 26 ( 1) 8 ( 1) Caucasian, non-Hispanic 661 (85) 1918 (91) 597 (92) Hispanic, African-American 0 1 (<1) 1 (<1) Hispanic, Asian 0 1 (<1) 0 Hispanic, Caucasian 42 ( 5) 71 ( 3) 17 ( 3) Hispanic, race unknown 8 ( 1) 6 (<1) 2 (<1) Missing 22 ( 3) 38 ( 2) 10 ( 2) Native American, non-Hispanic 3 (<1) 6 (<1) 0 Other 1 (<1) 2 (<1) 0 Pacific islander, non-Hispanic 0 1 (<1) 0

Recipient sex Female 293 (38) 993 (47) 267 (41) Male 484 (62) 1114 (53) 381 (59)

Karnofsky score 90-100% 466 (60) 1266 (60) 377 (58) <90% 209 (27) 627 (30) 201 (31) missing 102 (13) 214 (10) 70 (11)

Disease-related Disease status at transplant

ALL / AML advanced 152 (20) 613 (29) 0 ALL / AML early 303 (39) 986 (47) 0 ALL / AML intermediate 320 (41) 507 (24) 0 MDS NOS 0 0 7 (1) MDS advanced 0 0 288 (44) MDS early 0 0 353 (54) Missing 2 (<1) 1 (<1) 0

Transplant-related Stem cell source

Bone marrow 366 (47) 664 (32) 194 (30) Peripheral blood 411 (53) 1443 (68) 454 (70)

GvHD Prophylaxis

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ALL AML MDS Variable N (%) N (%) N (%)

No GVHD prophylaxis 2 (<1) 4 (<1) 1 (<1) Ex vivo T-cell depletion alone 11 (1) 12 ( 1) 1 (<1) Ex vivo T-cell depletion + post-TX immune suppression 22 (3) 17 ( 1) 4 ( 1) CD34 selection alone 0 1 (<1) 0 CD34 selection + post-TX immune suppression 1 (<1) 1 (<1) 1 (<1) Cyclophosphamide alone 4 ( 1) 12 ( 1) 3 (<1) Cyclophosphamide + others 0 1 (<1) 1 (<1) TACROLIMUS + MMF ± others 80 (10) 337 (16) 101 (16) TACROLIMUS + MTX ± others (except MMF) 345 (44) 1020 (48) 286 (44) TACROLIMUS + others (except MTX, MMF) 34 ( 4) 78 ( 4) 29 ( 4) TACROLIMUS alone 17 ( 2) 59 ( 3) 14 ( 2) CSA + MMF ± others (except TACROLIMUS) 36 ( 5) 183 ( 9) 53 ( 8) CSA + MTX ± others (except TACROLIMUS, MMF) 198 (25) 326 (15) 125 (19) CSA + others (except TACROLIMUS, MTX, MMF) 11 ( 1) 24 ( 1) 6 ( 1) CSA alone 6 ( 1) 16 ( 1) 8 ( 1) Other GVHD prophylaxis 10 ( 1) 16 ( 1) 15 ( 2)

Conditioning regimen Myeloablative 709 (91) 1491 (71) 431 (67) RIC 39 ( 5) 378 (18) 160 (25) NMA 17 ( 2) 152 ( 7) 30 ( 5) Other 12 ( 2) 86 ( 4) 27 ( 4)

Donor / recipient sex matching Donor male / recipient male 339 (44) 791 (38) 284 (44) Donor male / recipient female 163 (21) 611 (30) 180 (28) Donor female / recipient male 142 (18) 302 (15) 97 (15) Donor female / recipient female 124 (16) 367 (18) 87 (13) Unknown 9 (N/A) 36 (N/A) 0 (N/A)

Donor / recipient CMV serostatus Positive / negative 141 (18) 268 (13) 84 (13) Negative / postiive 232 (30) 770 (37) 221 (34) Negative 215 (28) 543 (26) 207 (32) Positive 162 (21) 425 (20) 120 (19) Unknown 27 ( 3) 101 ( 5) 16 ( 2)

Donor age at donation 10-19 23 ( 3) 51 ( 2) 18 ( 3) 20-29 279 (36) 806 (38) 262 (40) 30-39 273 (35) 693 (33) 199 (31) 40-49 165 (21) 432 (21) 129 (20) 50+ 37 ( 5) 125 ( 6) 40 ( 6) Median (range) 33 (18-61) 33 (18-61) 32 (19-60)

Donor race / ethnicity African-American, non-Hispanic 20 ( 3) 33 ( 2) 11 ( 2) Asian, non-Hispanic 9 ( 1) 28 ( 1) 5 ( 1) Caucasian, non-Hispanic 614 (79) 1653 (78) 539 (83) Hispanic, Caucasian 0 3 (<1) 0 Hispanic, race unknown 29 ( 4) 50 ( 2) 15 ( 2)

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ALL AML MDS Variable N (%) N (%) N (%)

Missing 88 (11) 321 (15) 70 (11) Native American, non-Hispanic 15 ( 2) 15 ( 1) 7 ( 1) Other 2 (<1) 2 (<1) 1 (<1) Pacific Islander, non-Hispanic 0 2 (<1) 0

Year of transplant 2000 50 ( 6) 83 ( 4) 18 ( 3) 2001 59 ( 8) 77 ( 4) 32 ( 5) 2002 57 ( 7) 76 ( 4) 31 ( 5) 2003 68 ( 9) 114 ( 5) 39 ( 6) 2004 79 (10) 218 (10) 52 ( 8) 2005 108 (14) 271 (13) 67 (10) 2006 127 (16) 305 (14) 83 (13) 2007 111 (14) 309 (15) 103 (16) 2008 63 ( 8) 218 (10) 35 ( 5) 2009 45 ( 6) 223 (11) 78 (12) 2010 10 ( 1) 167 ( 8) 53 ( 8) 2011 0 46 ( 2) 57 ( 9)


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Proposal 1711-97

Title: Imputation of KIR in Genome-Wide Association Study and the Association of KIR-HLA with Outcomes Following Allogeneic Hematopoietic Cell Transplant in Acute Myeloid Leukemia and Myelodysplastic Sydrome

Christine Camacho-Bydume: Memorial Sloan Kettering Cancer Center Lara Sucheston-Campbell: The Ohio State University Stephen Leslie: University of Melbourne Katharine Hsu: Memorial Sloan Kettering Cancer Center

Hypothesis: The study hypothesis is that statistical imputation techniques using single-nucleotide polymorphisms (SNPs) data can be applied to a genome-wide association study (GWAS) of allogeneic hematopoietic cell transplant (HCT) patients with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) in order to determine killer cell immunoglobulin-like receptor (KIR) gene and allele content.

Specific aims: The primary aim of this study is to impute KIR allotypes using KIR*IMP from SNP data available in the genome wide association study, DISCOVeRY-BMT (Determining the Influence of Susceptibility COnveying Variants Related to one-Year mortality after BMT).

The secondary aims of this study are: • To validate the imputation method in a cohort of patients by comparing KIR genotyping

determined by SNP imputation versus the use of well-established PCR-basedmethodologies.

• To analyze the association of combinations of the inhibitory KIR3DL1 and HLA-B alleleswith outcomes of relapse, survival, graft-versus-host disease (GVHD), and transplant-related mortality (TRM) following HLA-matched HCT in patients with AML and MDS.

• To determine whether allelic variation in KIR2DL1, -2DL2, and -2DL3 in combination withcognate HLA alleles is associated with outcomes of relapse, survival, GVHD, and TRMfollowing HCT for AML and MDS.

• To determine whether KIR-HLA allele combinations are associated with outcomes ofrelapse, survival, GVHD, and TRM following HLA-matched HCT in patients with acutelymphoblastic leukemia (ALL).

• To evaluate whether patient KIR-HLA combinations are associated with outcomes ofrelapse, survival, GVHD, and TRM following HCT for AML and MDS.

Scientific impact: The application of a high-throughput imputation method using SNP datasets from patients to obtain KIR genotyping has not previously been reported. Given the number of GWAS studies performed in various patient cohorts and the increasing data indicating relevance of natural killer (NK) immunogenetics in cancer control, the availability of a methodology to extract KIR genotyping will allow for rapid acquisition of data for clinical research, particularly in HCT. Although previous studies have suggested that activating KIR gene repertoires may predict clinical outcomes in patients with MDS, MDS has not been fully studied with KIR-HLA allele

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combinations to evaluate effects on outcome following HCT(1). Identification of KIR-HLA allele combinations associated with survival and relapse following HCT for AML and MDS will not only inform our understanding of anti-leukemic donor NK effects, but may also serve as the foundation for donor selection in the future to minimize disease relapse following HCT. Scientific justification: For patients undergoing allogeneic HCT for AML, disease relapse following transplant continues to represent a significant complication and has been found to contribute to 25-40% of deaths occurring after HCT for AML or MDS(2). NK cells exert a graft-versus-malignancy phenomenon(3). Central to calibrating the NK activation state are the inhibitory and activating KIRs via interactions with their cognate class 1 HLA ligands. Multiple reports now detail that KIR and HLA genotypes and allotypes determine the relative “activation threshold” for donor NK cells and thus predict the contribution of donor NK cells to the graft-versus-malignancy phenomenon. In the context of AML, KIR and HLA genotypes and allotypes can predict donors that are more likely to prevent relapse(2,3). Studying the association of KIR alleles with transplant outcomes has been limited by available KIR typing technology, such as KIR-SSP and KIR-SSOP, both labor-intensive processes. The application of imputation methodologies to SNP arrays and whole-exome sequencing has been successful for HLA, but were initially shown to be difficult to apply to the KIR region due to its high genetic variation in terms of gene content, gene arrangements, copy number, and polymorphisms(4,5). Recent advancements in imputation methods have now made their application to the KIR genes possible(5). The statistical imputation method, KIR*IMP, has been developed and validated to impute KIR gene content. KIR*IMP has been shown to achieve accuracy of over 98% for KIR copy number and haplotypes for the majority of KIR loci(5). Recently, the methodology has been further advanced to identify KIR allele content with high accuracy(6). The ability to apply this imputation method to GWAS data from patients allows for the opportunity to study genetic associations of KIR in various diseases. In our proposed study, we aim to apply the KIR*IMP methodology to the existing genome-wide association study (GWAS) DISCOVeRY-BMT to impute KIR allotypes for HCT patients with AML, MDS, and ALL and for their donors(7). This imputation method will be internally validated due to an overlap of 481 donor-recipient pairs in the DISCOVeRY-BMT cohort with a separate cohort provided by the National Marrow Donor Program (NMDP) that has previously been KIR gene and allele-typed by PCR-SSP(2). Availability of KIR allele resolution typing for a large cohort of patients will expand the current database for KIR-HLA association studies and help strengthen our understanding of KIR-HLA effects on outcomes of survival and relapse for patients with AML, MDS, and ALL. Study population: The study population will include patients greater than 18 years old diagnosed with AML, MDS, and ALL reported to CIBMTR who received a HLA-matched allogeneic HCT from a related or unrelated donor between 2000 - 2011. Data requirements: The proposed study does not require collection of any supplemental data outside of the current data collection forms as follows: Recipient Baseline Data (2000), Infectious Disease Markers (2004), Confirmation HLA Typing (2005), Acute Myelogenous Leukemia Pre-HCT Data (2010), MDS/MPD Pre-HCT Data (2014), Acute Myelogenous Leukemia Post-HCT Data (2110), MDS/MPD Post-HCT Data (2114), Pre-Transplant Essential Data (2402), and Post-Transplant Essential Data

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(2450). Variables to be analyzed: Patient-related:

- Age: 18-29 vs. 30-39 vs. 40-49 vs. 50-59 vs. ≥ 60 - Gender: male vs. female - Karnofsky score: <90 vs. 90-100%

Disease-related:

- Diagnosis: AML vs. MDS vs. ALL - Disease status at transplant: early vs. advanced

Transplant-related:

- Donor and recipient HLA typing and degree match - Year of transplant: 2000-2011 - Condition regimen intensity: myeloablative vs. non-myeloablative - Donor-recipient gender match: M/M vs. M/F vs. F/M vs. F/F - Source of stem cells: bone marrow vs. peripheral blood - Donor type: HLA identical sibling vs. well-matched unrelated - Type of graft versus host disease (GVHD) prophylaxis: T-cell depleted graft vs. non-

depleted - Post-transplant survival status: alive vs. dead

Sample requirements: This study does not require the use of biological specimens. Study design: For our proposed study, we will initially use the newly updated statistical imputation method of KIR*IMP to impute KIR allotypes for 2,888 European American AML, MDS, and ALL patients and their HLA-matched unrelated donors who received allogeneic HCT from 2000 – 2011 (for which 211 recipients-donors are matched at 8/8 and the remainder are matched at 10/10 loci). We will then internally validate the KIR imputation method for a cohort of over 400 donor-recipient pairs for whom KIR typing has been previously obtained by PCR-SSP. The large sample size of donor-recipient pair overlap between these two cohorts will provide a plethora of data to accurately validate the KIR imputation method, especially for its ability to capture less common KIR alleles. Upon completion of the imputation of KIR typing for the entire GWAS cohort, we will then examine donor KIR/HLA and its association with relapse, survival, GVHD, and TRM for patients with AML. We also will analyze the association of donor KIR/HLA with the outcomes of relapse, survival, GVHD, and TRM for patients with MDS following HCT. Based on previous research, we anticipate that the AML and MDS cohorts will have similar results in terms of their associations of donor KIR/HLA with outcomes following HCT. If these predictions are confirmed, we will combine the AML and MDS cohorts for our analysis. Since previous studies have primarily focused on donor KIR/HLA, we will subsequently examine whether patient KIR/HLA has an association with outcomes of relapse, survival, GVHD, and TRM following HCT. We also will investigate the association of donor KIR/HLA and the outcomes of relapse, survival, GVHD, and TRM in patients with ALL following HCT. However, we predict that there will be no KIR/HLA association demonstrated within this cohort. Therefore, the ALL cohort may serve as a

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comparison for the AML/MDS cohort. This data will be included in an already existing NIH-funded KIR/HLA project with the International Histocompatibility Working Group (IHWG). Biostatistician support is not requested from CIBMTR for our proposed project given the long-standing collaboration with biostatisticians within the IHWG for the previously established KIR/HLA project. Data source: This study will use the CIBMTR Research Database. References: 1. Stringaris, K, Marin D, Barrett AJ, et al. KIR gene haplotype: an independent predictor of clinical outcomes in MDS patients. Blood 2016;128(24):2819-2823. 2. Boudreau JE, Giglio F, Gooley TA, et al. KIR3DL1/ HLA-B subtypes govern acute myelogenous leukemia relapse after hematopoietic cell transplantation. J Clin Oncol 2017;35(20):2268-2278. 3. Venstrom JM, Pittari G, Gooley TA, et al. HLA-C-dependent prevention of leukemia relapse by donor activating KIR2DS1. N Engl J Med 2012;367(9):805-16. 4. Middleton D, Gonzalez F. The extensive polymorphism of KIR genes. Immunology 2010;129(1):8-19. 5. Vukcevic D, Traherne JA, Naess S, et al. Imputation of KIR types from SNP variation data. Am J Hum Genet. 2015;97(4):593-607. 6. Leslie S. Imputation of HLA and KIR from GWAS data. The 17th International HLA & Immunogenetics Workshop. 2017. Asylomar, CA. 7. Karaesmen E, Rizvi AA, Press LM, et al. Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant. Blood 2017;130(13):1585-1596.

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https://www.ncbi.nlm.nih.gov/pubmed/?term=kir3dl1%252Fhla-b+subtypes+govern

https://www.ncbi.nlm.nih.gov/pubmed/?term=HLA-C-dependent+prevention+of+leukemia


Table 1. Characteristics of AML and MDS patients for DISCOVeRY BMT cohort

AML MDS Variable N (%) N (%) Number of Recipients 2107 648 Patient-related Recipient age at transplant

0-9 80 ( 4) 32 ( 5) 10-19 139 ( 7) 32 ( 5) 20-29 255 (12) 39 ( 6) 30-39 223 (11) 58 ( 9) 40-49 439 (21) 104 (16) 50-59 560 (27) 218 (34) 60+ 411 (20) 165 (25) Median (range) 48 (1-78) 53 (0-74)

Recipient race / ethnicity Caucasian, non-Hispanic 1918 (91) 597 (92) African-American, non-Hispanic 37 ( 2) 13 ( 2) Asian, non-Hispanic 26 ( 1) 8 ( 1) Pacific Islander, non-Hispanic 1 (<1) 0 Native American, non-Hispanic 6 (<1) 0 Hispanic, Caucasian 71 ( 3) 17 ( 3) Hispanic, African-American 1 (<1) 1 (<1) Hispanic, Asian 1 (<1) 0 Hispanic, race unknown 6 (<1) 2 (<1) Missing 38 ( 2) 10 ( 2) Other 2 (<1) 0

Recipient sex Female 993 (47) 267 (41) Male 1114 (53) 381 (59)

Karnofsky score 90-100% 1266 (60) 377 (58) <90% 627 (30) 201 (31) Missing 214 (10) 70 (11)

Disease-related Disease status at transplant

AML advanced 613 (29) 0 AML early 986 (47) 0 AML intermediate 507 (24) 0 MDS NOS 0 7 ( 1) MDS advanced 0 288 (44) MDS early 0 353 (54) Missing 1 (<1) 0


Bone marrow 664 (32) 194 (30)

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AML MDS Variable N (%) N (%)

Peripheral blood 1443 (68) 454 (70) GvHD Prophylaxis

No GVHD prophylaxis 4 (<1) 1 (<1) Ex vivo T-cell depletion alone 12 ( 1) 1 (<1) Ex vivo T-cell depletion + post-TX immune suppression 17 ( 1) 4 ( 1) CD34 selection alone 1 (<1) 0 CD34 selection + post-TX immune suppression 1 (<1) 1 (<1) Cyclophosphamide alone 12 ( 1) 3 (<1) Cyclophosphamide + others 1 (<1) 1 (<1) Tacrolimus + MMF ± others 337 (16) 101 (16) Tacrolimus + MTX ± others (except MMF) 1020 (48) 286 (44) Tacrolimus + others (except MTX, MMF) 78 ( 4) 29 ( 4) Tacrolimus alone 59 ( 3) 14 ( 2) CSA + MMF ± others (except Tacrolimus) 183 ( 9) 53 ( 8) CSA + MTX ± others (except Tacrolimus, MMF) 326 (15) 125 (19) CSA + others (except Tacrolimus, MTX, MMF) 24 ( 1) 6 ( 1) CSA alone 16 ( 1) 8 ( 1) Other GVHD prophylaxis 16 ( 1) 15 ( 2)

Conditioning regimen Myeloablative 1491 (71) 431 (67) RIC 378 (18) 160 (25) NMA 152 ( 7) 30 ( 5) To be determined 86 ( 4) 27 ( 4)

Donor / recipient sex matching Male / male 791 (38) 284 (44) Male / female 611 (30) 180 (28) Female / male 302 (15) 97 (15) Female / female 367 (18) 87 (13) Unknown 36 (N/A) 0 (N/A)

Donor / recipient CMV serostatus Positive / negative 268 (13) 84 (13) Negative / positive 770 (37) 221 (34) Negative 543 (26) 207 (32) Positive 425 (20) 120 (19) Unknown 101 ( 5) 16 ( 2)

Donor age at donation 18-19 51 ( 2) 18 ( 3) 20-29 806 (38) 262 (40) 30-39 693 (33) 199 (31) 40-49 432 (21) 129 (20) 50+ 125 ( 6) 40 ( 6) Median (range) 33 (18-61) 32 (19-60)

Donor race / ethnicity Caucasian, non-Hispanic 1653 (78) 539 (83)

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AML MDS Variable N (%) N (%)

African-American, non-Hispanic 33 ( 2) 11 ( 2) Asian, non-Hispanic 28 ( 1) 5 ( 1) Pacific Islander, non-Hispanic 2 (<1) 0 Native American, non-Hispanic 15 ( 1) 7 ( 1) Hispanic, Caucasian 3 (<1) 0 Hispanic, race unknown 50 ( 2) 15 ( 2) Other 2 (<1) 1 (<1) Missing 321 (15) 70 (11)

Year of transplant 2000 83 ( 4) 18 ( 3) 2001 77 ( 4) 32 ( 5) 2002 76 ( 4) 31 ( 5) 2003 114 ( 5) 39 ( 6) 2004 218 (10) 52 ( 8) 2005 271 (13) 67 (10) 2006 305 (14) 83 (13) 2007 309 (15) 103 (16) 2008 218 (10) 35 ( 5) 2009 223 (11) 78 (12) 2010 167 ( 8) 53 ( 8) 2011 46 ( 2) 57 ( 9)

Follow-up among survivors, months N Eval 892 299 Median (range) 36 (1-126) 36 (3-124)

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Proposal 1711-03

Title: Effect of HLA Phenotypes on Long Term GVHD Risk

Charlotte McIlwaine Story: University of North Carolina School of Medicine Marcie L. Riches: The University of North Carolina at Chapel Hill Paul M. Armistead: The University of North Carolina at Chapel Hill, Department of Medicine, Division of Hematology/Oncology

Hypothesis: HLA genotypes can be stratified according to the frequency with which they bind small peptide fragments as part of Class I and II MHC antigen presentation. These stratifications may predict the development of Graft-versus-Host Disease (GVHD) and Graft-versus-Tumor Effect (GvT) in patients receiving HLA-identical related or unrelated donor allogeneic hematopoietic cell transplants (alloHCT).

Specific aims: GVHD remains a significant cause of morbidity and mortality in patients receiving alloHCT, even in the setting of full HLA match. If this model reliably identifies patients groups at higher risk for GVHD or relapse in the setting of full HLA match, it could be a useful tool for clinicians choosing conditioning therapies and/or GVHD prophylaxis for patients undergoing alloHCT.

The following outcomes will be examined separately in related and unrelated donors: • Overall survival: defined as time from transplant until date of last contact.• Acute GVHD: measured as a cumulative incidence competing risk analysis with relapse or death as

competing events.• Chronic GVHD: measured as a cumulative incidence competing risk analysis with relapse or death as

competing events.• Relapse: measured as a cumulative incidence competing risk analysis with death as the competing

event.

Scientific impact: While graft-versus-host disease is a treatable, and to some extent preventable, complication of hematopoietic stem cell transplants (HCT), it significantly contributes to the morbidity and mortality of alloHCT. Minor histocompatibility antigens are presented to donor T cells by recipient major histocompatibility complexes (MHCs) leading to activation of the donor T cells within the recipient’s blood stream. The cytotoxic response generated by donor T cells against the recipient cells damages the recipient’s tissues and causes the symptoms of Graft-versus-Host Disease (GVHD).1 Ensuring that donor-recipient pairs are HLA-matched can reduce the likelihood and severity of GVHD, and improve survival. However, GVHD still develops in patients that receive a fully matched transplant.2

If HLA genotyping can be used to estimate how well a donor-recipient pair’s (DRP) MHCs present minor histocompatibility antigens to the lymphocytes that mediate GVHD, then HLA genotyping could be used to reliably identify patient groups at higher risk for GVHD or relapse in the setting of full HLA match. This would provide a useful tool for clinicians choosing conditioning therapies for patients undergoing alloHCT.

Scientific justification:

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Data from labs of Paul Armistead and Ben Vincent lab show that certain HLA genotypes bind peptide epitopes with varying degrees of efficiency. By quantifying the cumulative effect of a patient’s HLA genes (HLA-A, -B, -C, -DRβ1), one could estimate the cumulative peptide binding efficiency of a donor-recipient pair’s MHCs. That is to say, HLA genotyping could be used to estimate how well a donor-recipient pair’s MHCs present minor histocompatibility antigens to the lymphocytes that mediate GVHD. These genetic differences in peptide binding have also been demonstrated in vivo by Abelin et al.3 Note: in the images that follow, DRP stands for donor-recipient pair. Figure 1A.

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Figure 1B.

Figure A shows that many Class II HLA can present a higher fraction of antigenic peptides compared to Class I HLA alleles. Figure IB allows one to appreciate the greater than one order of magnitude difference in the number of peptides that can bind with high affinity to various Class I HLA alleles.

Figure 2

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Figure 2 allows one to appreciate the greater than one order of magnitude difference in the number of peptides that can bind with high affinity to various Class I HLA alleles.

Additionally, in our original study, we used HLA alleles and peptides contained in a patient cohort from MD Anderson Cancer Center. These are the alleles seen in the figures above. These represent the most common HLA Class I alleles in the US population. In our pilot study, we initially collected data on 313 transplant patients. For our analysis, we required that patients have a minimum of 4/6 informative HLA Class I alleles. Of the qualifying transplant patients, 6% had all 6 alleles that were informative. 22% had a 5 alleles that were informative. 27% had 4 alleles that were informative. We normalized our cumulative peptide affinity score to the number of informative alleles. We could computationally evaluate all HLA Alleles that have been analyzed by netMHCpan and netMHCpanII to expand our number of informative phenotype. This approach was not originally taken due to computational time constraints.

Figure 3

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In our initial study the low, medium, and high phenotypes showed a normal distribution, shown in Figure 3. In the histogram of HLA Class I binding (all), one can view the normal distribution phenotypes of all qualifying adult transplant patients at UNC Chapel Hill. Because of this normal distribution and no clear clustering, we used tertiles in our initial statistical analysis. Donor-recipient pairs can be grouped according to their cumulative peptide-binding fraction into tertiles: high-binding, moderate-binding, and low-binding. This project will compare outcomes between these three groups. We hypothesize that recipients with HLA-genotypes that predict greater cumulative peptide binding will experience higher rates of Graft-versus-Host Disease. However, since there are multiple factors that can contribute to transplant outcomes including the risk of GVHD, these known variables will be included in the analysis. In prior work, we used the informative HLA Class I alleles to categorize patients according to cumulative peptide binding affinity.

By quantifying the cumulative effect of a patient’s HLA alleles, we can categorize these patients into high-binding, moderate-binding, and low-binding. With a larger number of patients, we can separately analyze impact in unrelated and related donors. This is particularly important because the transplants from unrelated donors are affected by higher numbers of minor histocompatibility antigens. Study population: Patients who received fully matched allogeneic hematopoietic stem cell transplant from 01-01-2010 to 12-31-16 reported to the CIBMTR will be stratified into three groups (low-binding, moderate-binding, high-binding) according to cumulative peptide binding fraction (determined by HLA typing).

Low: 0.3198 (range, 0.1730 – 0.3790) Mid: 0.4380 (range, 0.3802 – 0.5165) Hi: 0.5604 (range, 0.5248 – 0.7920)

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Variables to be analyzed: Inclusion:

• Patients who received a first allogeneic stem cell transplant for malignant disease • Fully matched related or unrelated donor recipient pair by high resolution HLA typing • Patients receiving peripheral blood stem cells or marrow stem cells only • Patients can receive any conditioning regimen • Patients will have received calcineurin-inhibitor based GVHD prophylaxis

Exclusion:

• Patients with aplastic anemia • Patients with non-malignant disorders such as inherited disorders • Patients with prior allogeneic transplant • Mismatched transplant • Umbilical cord blood transplant

Patient-related variables:

• Age: continuous; by decade • Gender: male vs. female • Hematopoietic cell transplantation (HCT)-specific co-morbidity index at time of transplant4 • HLA typing

Disease-related variables:

• Disease: ALL/AML/CLL/CML/lymphoma/MDS • Disease status at transplant • Disease risk index5

Transplant-related variables:

• Donor type: related vs. unrelated • Donor gender match: F M vs. Other • Source of stem cell: BMT vs PBSC • GVHD prophylaxis: ATG+/- • Conditioning intensity: myeloablative vs. non-myeloablative /reduced intensity • ABO compatibility: major/minor/bidirectional • CMV status: donor positive, recipient positive/ donor negative, recipient positive/ donor

positive, recipient negative/ donor negative, recipient negative Data requirements: Data previously collected on the CIBMTR database are requested. No supplemental data are required. Sample requirements: No samples are being requested. Study design: Standard data collected on CIBMTR forms are requested. Descriptive tables of patient-, disease-, and transplant-related factors will be prepared. These tables will list median and range for continuous variables and percent of total for categorical variables. Patient-,

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disease- and transplant-related factors will be compared between groups using the Chi-square test for categorical variables and the Wilcoxon two sample test for continuous variables. Overall survival will be determined using the Kaplan-Meier estimator. Outcomes of GVHD, engraftment, and relapse will be measured using cumulative incidences estimates to account for competing risks. Multivariable models for the outcomes of acute GVHD, chronic GVHD, relapse and survival will be performed using the Cox proportional hazards model with the main effect variable of Low-binding/Moderate-binding/High-binding forced into the model. The proportional hazards assumption will be examined. If violated, it will be added as a time-dependent covariate. The stepwise selection procedure will be used to select significant covariates. An interaction between the main effect and significant covariates will be tested. It may be useful to identify a discovery and a validation cohort to first define the low, middle and high affinity cut points in discovery cohort and then test in the validation cohort. Alternatively, given the potentially large patient population, we could consider assessing the predicted binder estimate as a continuous variable first and then determine if there are potential threshold cut-points. The initial study utilized the tertile-approach based upon # of patients and normal distribution. We are certainly open to more sophisticated statistical models to maximize this analysis and look forward to working with the CIBMTR statisticians to define the optimal analysis. Non-CIBMTR data source: The only data source will be previously collected data from the CIBMTR Research Database.

References: 1. Shlomchik WD. Graft-versus-Host disease. Nat Rev Immunol 2007 May;7(5):340-352. doi:10.1038/nri2000. 2. Lee SJ, Klein J, Haagenson M, et al. High-Resolution donor-Recipient HLA matching contributes to the success of unrelated donor marrow transplantation. Blood 2007 Dec 15; 110(13):4576-4583. doi:10.1182/blood-2007-06-097386. 3. Abelin JG, Keskin DB, Sarkizova S, et al. Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-Allelic Cells Enables More Accurate Epitope Prediction. Immunity 2017 Feb 21;46(2):315-326. doi:10.1016/j.immuni.2017.02.007. 4. Sorror M L, Maris MB, Storb R, et al. Hematopoietic cell transplantation (HCT)-Specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood 2005 Oct 15;106(8):2912-2919. doi:10.1182/blood-2005-05-2004. 5. Armand P, Kim HT, Logan BR, et al. Validation and refinement of the Disease Risk Index for allogeneic stem cell transplantation. Blood 2014 Jun 5;123(23):3664-3671. doi:10.1182/blood-2014-01-552984.

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Table 1. Characteristics of Patients with Malignant Disease receiving a First Allogeneic, 8/8 HLA-Matched Donor Transplant

Variable N (%) Number of Recipients 7678 Number of centers 158 Patient-related Recipient age at transplant 0-9 262 ( 3) 10-19 351 ( 5) 20-29 635 ( 8) 30-39 710 ( 9) 40-49 1087 (14) 50-59 1871 (24) 60+ 2762 (36) Median (range) 55 (0-84) Recipient sex

Male 4388 (57) Female 3290 (43)

Disease-related Disease at transplant

AML 3547 (46) ALL 1203 (16) Other leukemia 221 ( 3) CML 252 ( 3) MDS 1698 (22) Other acute leukemia 82 ( 1) NHL 554 ( 7) Hodgkins Lymphoma 68 ( 1) Plasma cell disorders, MM 52 ( 1) Other malignancies 1 (<1)

Transplant-related Donor type

8/8 matched HLA-identical siblings 558 ( 7) 8/8 matched unrelated donors 7120 (93)

Stem cell source Bone marrow 1421 (19) Peripheral blood 6255 (81) BM+PBSC 2 (<1)

GvHD prophylaxis Tacrolimus + MMF ± others 1210 (16) Tacrolimus + MTX ± others (except MMF) 4593 (61) Tacrolimus + others (except MTX, MMF) 585 ( 8)

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Variable N (%) Tacrolimus alone 184 ( 2) CSA + MMF ± others (except Tacrolimus) 492 ( 6) CSA + MTX ± others (except Tacrolimus, MMF) 444 ( 6) CSA + others (except Tacrolimus, MTX, MMF) 33 (<1) CSA alone 37 (<1) Unknown 100 (N/A)

Conditioning regimen Myeloablative 3347 (44) RIC 1471 (19) Nonmyeloablative 174 ( 2) Other 2686 (35)

Donor / recipient sex matching Donor male / Recipient male 3169 (41) Donor male / Recipient female 2014 (26) Donor female / Recipient male 959 (12) Donor female / Recipient female 1079 (14) To be determined / missing 457 ( 6)

Donor / recipient CMV serostatus Negative / negative 2023 (26) Negative / positive 2617 (34) Positive / negative 664 ( 9) Positive / positive 1838 (24) Unknown 536 ( 7)

Donor age at donation To be determined 7334 (96) 0-9 7 (<1) 10-19 19 (<1) 20-29 36 (<1) 30-39 46 ( 1) 40-49 75 ( 1) 50+ 161 ( 2) Median (range) 49 (4-73)

Year of transplant 2010 1061 (14) 2011 1039 (14)

2012 1105 (14) 2013 1351 (18) 2014 1474 (19) 2015 1453 (19) 2016 195 ( 3)

Follow-up among survivors, months N Eval 1875

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Variable N (%) Median (range) 36 (1-92)

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Proposal 1711-71

Title: The Impact of HLA Class I Risk Alleles Associated with AA Immune Pathogenesis on Allogeneic Transplant Outcomes in Patients with Severe Acquired Aplastic Anemia.

Daria V. Babushok: University of Pennsylvania Timothy S. Olson: Children’s Hospital of Philadelphia

Hypothesis: The rationale for this study, based on previous data from our group and others1-4, is that specific Human Leukocyte Antigen (HLA) Class I alleles are linked to acquired aplastic anemia (AA) immune pathogenesis, and that patients with such AA HLA “risk” alleles have a more severe disease course and have an increased risk of clonal evolution3. Our central hypothesis is that by defining HLA Class I risk alleles across a large, ethnically diverse cohort of AA patients available from the CIBMTR, we will be able to define unique risk profiles for outcomes such as graft rejection and relapse after HSCT based on presence of specific HLA Class I alleles.

Specific aims: • To identify the immunogenetic determinants of aplastic anemia pathogenesis in a large ethnically

diverse cohort of AA patients. Our working hypothesis for this aim, supported by our previousstudies3-4, is that HLA class I alleles linked to AA autoimmunity will be over-represented in AApatients, as compared to ethnically-matched controls, and will be subject to somatic loss in AApatients. The CIBMTR database with its large cohort of AA patients and accompanying high-resolution HLA typing is a powerful resource that is uniquely positioned to define the HLAdeterminants of AA autoimmunity. Here, we propose to extend our previous institutional cohortstudy of 66 AA patients to identify all common HLA class I AA risk alleles in a large ethnically diversecohort of AA. We will do this via two subaims:

• To identify HLA alleles that are disproportionately more common in patients with AA ascompared to ethnically-matched population-based controls and patients withmyelodysplastic syndrome (MDS).

• To identify all common HLA class I alleles that are targeted by clonal somatic loss inhematopoietic cells of AA patients, thus directly implying their significance in AApathogenesis.

• To define the impact of AA HLA risk alleles on allogeneic transplant outcomes in severe aplasticanemia. Our working hypothesis for this aim, supported by our recent study3, is that AA HLA riskalleles are pathogenically related to disease severity and thus are likely associated with a higherrisk of graft failure, relapse, or GVHD post-transplant. We will compare the HSCT outcomesbetween AA patients with and without AA HLA risk alleles.

Scientific impact: AA is a rare life-threatening autoimmune blood disease that affects previously healthy children and adults and causes an inability to make sufficient blood cells for normal life5,6. If untreated, most patients succumb to infections or bleeding. Although advances in supportive care, immunosuppression, and bone marrow transplantation have improved outcomes of AA patients, for many patients the prognosis remains poor7. The main non-transplant therapy is lymphocyte-directed immunosuppression therapy (IST), which restores adequate blood production in about two thirds of patients8,9; however,

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complete remissions are rare, a third of the patients do not respond to frontline therapy, a third of those who respond will relapse8-10, and ~15% of patients develop MDS or leukemia11. Although outcomes of HSCT in AA have improved, outcomes for older patients remain suboptimal, with 10-year overall survival in patients over 40 years of ~50%7. There is a critical need for novel approaches to treatment and prevention of AA and its complications. Major barriers to progress have been the poor understanding of mechanisms of autoimmunity in AA. Existing studies have been limited by small numbers of patients in single or multi-institutional cohorts. Our proposal will address these crucial knowledge gaps in two primary ways: − The proposed studies will take advantage of the powerful resource of CIBMTR, which includes a

large number of patients with AA to illuminate the mechanism of AA autoimmunity. We will use the available HLA typing information, together with HLA allele mutation analysis to identify HLA risk alleles associated with high risk AA. These findings will open the door to future studies of common HLA peptide binding motifs, and are likely to lead to improved therapies in the future.

− The proposed studies will evaluate the role of HLA risk alleles on HSCT outcomes. We anticipate that this study will identify a group of AA patients who may be at a higher risk of graft failure and AA relapse, who may benefit from improved conditioning and personalized post-transplant surveillance approaches.

Scientific justification: Multiple lines of evidence support the immune-mediated etiology of AA5, the strongest of which is the clinical efficacy of T-lymphocyte directed therapy in AA patients12,13. Recently, in studies of clonal hematopoiesis in AA2,4,14-19, we and others found recurrent clonal expansion of cells with loss of HLA alleles4,14. These observations prompted us to analyze the HLA region in 66 patients with AA using deep NGS optimized to accurately capture somatic mutations in HLA Class I alleles3. We found that 17% of AA patients had somatic HLA loss, which included 13 loss-of-function mutations targeting 4 HLA class I alleles (risk alleles) as well as loss of HLA through chromosome 6p copy number-neutral loss of heterozygosity (6p CN-LOH). The most frequently mutated alleles (HLA-B*14:02 and HLA-B*40:02) occurred at a significantly higher frequency in AA patients compared to ethnically-matched controls or to patients with MDS also seen at our center (Table 1). Patients with AA risk alleles had a more severe clinical course, were less responsive to therapy, and were more likely to develop MDS-associated mutations. Additionally, an independent study by Zaimoku, et al. also identified recurrent loss of HLA- B*40:02 in 28 Japanese AA patients1, pointing to a common pathogenic role of HLA-B*40:02 in AA patients of different ethnicities. Interestingly, Japan has a higher frequency of HLA-B*40:02, and incidence of AA is also 2-3-fold higher in East Asia20. Jointly, our data strongly implicate HLA-class I-mediated autoantigen presentation in AA pathogenesis and provide a robust link between t he AA patients’ HLA alleles and subsequent differences in the patient’s AA course, including response to treatment and complications. Study population: Aim 1A (HLA association study) and Aim 2 (AA transplant outcomes)

• Patients with Severe Acquired Aplastic anemia (SAA), believed to be immune-mediated. • Exclusion criteria: Known alternative etiologies for aplastic anemia: e.g. inherited bone marrow

failure (e.g. Fanconi Anemia, Diamond Blackfan Anemia), or known medication-associated aplasia (e.g. chemotherapy-associated).

Subset of Cohort 1, with available pre-transplant peripheral blood samples. • Suitable samples could include DNA extracted from peripheral blood, whole blood, cryo-

preserved whole blood, or granulocytes.

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Patients with MDS with available ethnicity/race and high-resolution HLA allele information to serve as matched controls in Aim 1A. Data requirements: Baseline recipient data: Date of diagnosis, Bone marrow histology (performed-yes/no, report if available), Etiology of aplastic anemia as reported in Data Collection Form, Sensitivity to DNA crosslinking agents/FA (testing - yes/no, outcome), Testing for PNH (testing-yes/no, outcome), Blood counts prior to therapy (WBC, ANC, reticulocyte count, Hgb, Plt), Cytogenetics (if available), pre-HCT disease treatment, Demographics (gender, age, race/ethnicity), HLA typing of patient (high resolution, if available). Transplant Procedure and Outcomes: HLA typing of donor, conditioning regimen, graft source, mobilization, T-cell depletion and other graft manipulation, GVHD prophylaxis, co-existing diseases, pre-HSCT infection, HSCT history. Follow-up recipient data/Transplant outcomes: Survival, neutrophil and platelet engraftment, acute and chronic GVHD, relapse, Immune reconstitution, Chimerism, Infection, Organ function, Subsequent HCT or donor cell infusion (DCI), New malignancy, Cause of death. Data requested for MDS control cohort: Demographics (gender, age, race/ethnicity), HLA typing. Sample requirements: Suitable samples include DNA extracted from peripheral blood, whole blood, cryopreserved whole blood, or granulocytes. To ensure sufficient DNA for quality control, HLA NGS, SNP-A and validation, 3 µg of DNA is requested, but a minimum of 1 µg will be acceptable, with HLA NGS prioritized for less abundant DNA samples. Study design: Specific Aim 1: To identify the immunogenetic determinants of aplastic anemia pathogenesis in a large ethnically diverse cohort of AA patients.

• 1A. AA HLA Allele Association Analysis HLA allele frequencies for AA patients will be calculated separately for each ethnic/racial group and will be compared to their corresponding frequencies in the ethnically-stratified control populations from The Allele Frequency Net Database, a database and repository for immune gene frequencies in worldwide populations21, as well as to CIBMTR patients with MDS. The analysis will be performed using Fisher’s exact test or Chi-squared test, as applicable, with a two-tailed significance level of 0.05. Alleles found to be significantly over-represented in AA will be further analyzed to determine the most common haplotypes in the respective ethnic populations, using haplotype frequencies reported in The Allele Frequency Net Database21.

• 1B. Identification of HLA class I alleles selected for somatic loss in AA patients. In Aim 1B, we will extend our recent study3, in which we evaluated 66 AA patients from our institutional cohort for HLA Class I somatic loss, to identify common AA HLA class I risk alleles in a larger, ethnically heterogeneous AA cohort. We will use our established analysis pipeline, which includes deep targeted next-generation sequencing (NGS) of HLA class I alleles and Single Nucleotide Polymorphism Array (SNP-A) genotyping. AA HLA risk alleles will be inferred from alleles targeted by loss-of-function somatic mutations as previously described3. Importantly, the use of somatic loss-of-function mutations to identify pathogenic HLA alleles powerfully complements the genetic association approach in Aim 1A, which by itself is not able to distinguish indirect associations from true causal variants.

• All assays, including DNA extraction, deep targeted NGS of HLA class I region, HLA allele

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identification and somatic mutation calling, and SNP-A analysis will be done using our established analysis pipeline as previously described3. As requested in the instructions, the detailed description of the proposed testing methodology, the summary of the investigators’ previous experience with the proposed assay systems, and biosketches documenting experience in the laboratory methods proposed are included in the appendix.

Specific Aim 2: To define the impact of AA HLA risk alleles on allogeneic transplant outcomes in severe aplastic anemia.

• Patient Population. We will analyze transplant outcomes in all patients with AA whoreceived bone marrow (BM) grafts from unrelated or HLA-matched sibling donors. BecauseBM grafts have been previously shown to have superior outcomes compared to peripheralblood stem cell grafts in SAA, we will restrict this analysis to BMT.Secondly, as an exploratory subaim, we will extend our analysis to patients with AA whoreceived mismatched or haploidentical HSCT. Although we expect the number of patients inthis category to be small, this comparison could give important preliminary data for futurestudies investigating whether AA patients who receive allografts from a donor lacking sharedAA HLA risk alleles (e.g. in haploidentical HSCT setting) have lower rates of relapse or graftfailure.

Endpoints: The primary endpoints will be rates of graft failure and relapse. Secondary endpoints will be overall survival, and rates of acute and chronic GVHD (grades 2-4). Blood count recovery will be defined as previously described22, with neutrophil recovery defined as achieving an absolute neutrophil count of ≥ 0.5 × 109/L for 3 consecutive days, and platelet recovery as a platelet count ≥ 20 × 109/L at least 7 days after last platelet transfusion.

Statistical analysis: Patients will be stratified by presence or absence of at least one identified risk allele, which will include the 4 previously identified HLA risk alleles (HLA-A*33:03, HLA-A*68:01, HLA-B*14:02, and HLA-B*40:02)3 as well as risk alleles we will identify in Aim 1 of this proposal. Within each risk allele group (i.e. those with or without risk alleles), the analyses will be further separated by donor type (matched related or unrelated donor). We will compare the patient, disease and transplant-related characteristics using the Chi-squared or Fisher’s exact test, as appropriate. The probability of overall survival will be analyzed using Kaplan-Meier estimator. Multivariate analysis will be performed using Cox regression method, including variables such as age, sex, comorbidity index, type of ATG used, conditioning regimen, time from diagnosis to transplant, HLA match, type of GVHD prophylaxis, and binned range of year the transplant was performed. A two-tailed significance level of P< 0.05 will be used.

Expected results, possible pitfalls and alternatives: Based on our previous data3, we expect to identify a number of AA HLA predisposition alleles, with ~10% of AA patients having loss-of-function HLA class I mutations, and ~12% having 6p CN-LOH. We expect that patients with AA HLA risk alleles will have more immune-mediated HSCT complications, including graft failure and AA relapse. Pitfalls and Alternatives: 1) The proposed experiments use our established analytical pipeline3, and we anticipate no technical difficulties carrying out proposed assays and analyses. 2) AA is rare and sample size has been a historical challenge. The studies in this application overcome these difficulties by taking advantage of the largest AA registry and sample repository. With the incidence of graft failure in AA ranging up to 17%23,24, the CIBMTR cohort should allow for detection of a clinically meaningful difference between the patient groups.

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Non-CIBMTR data source: Not applicable References: 1. Zaimoku Y, Takamatsu H, Hosomichi K, Ozawa T, Nakagawa N, Imi T, et al. Identification of an HLA class I allele closely involved in the auto-antigen presentation in acquired aplastic anemia. Blood 2017 May 25;129(21)2908-2916. 2. Katagiri T, Sato-Otsubo A, Kashiwase K, Morishima S, Sato Y, Mori Y, et al. Frequent loss of HLA alleles associated with copy number-neutral 6pLOH in acquired aplastic anemia. Blood 2011 Dec 15;118(25):6601-6609. 3. Babushok DV, Duke JL, Xie HM, Stanley N, Atienza J, Perdigones N, et al. Somatic HLA Mutations Expose the Role of Class I-Mediated Autoimmunity in Aplastic Anemia and its Clonal Complications. Blood Advances 2017 Oct 10;1(22):1900-1910. 4. Betensky M, Babushok D, Roth JJ, Mason PJ, Biegel JA, Busse TM, et al. Clonal evolution and clinical significance of copy number neutral loss of heterozygosity of chromosome arm 6p in acquired aplastic anemia. Cancer Genet 209, 1-10 (2016). 5. Young NS. Current concepts in the pathophysiology and treatment of aplastic anemia. Hematology Am Soc Hematol Educ Program 2013; 2013:76-81. 6. Babushok DV, Bessler M. Aplastic Anemia and Pure Red Cell Aplasia. Anemia: Pathophysiology, Diagnosis and Management (eds. Benz, E.J., Berliner, N. & Schiffman, F.J.) Cambridge University Press, 2018. 7. Bacigalupo A. How I treat acquired aplastic anemia. Blood 2017 Marc 16;129(11):1428-1436. 8. Scheinberg P, Nunez O, Weinstein B, Biancotto A, Wu CO, Young NS. Horse versus rabbit antithymocyte globulin in acquired aplastic anemia. N Engl J Med 2011;365:430-438. 9. Townsley DM, Scheinberg P, Winkler T, Desmond R, Dumitriu B, Rios O, et al. Eltrombopag Added to Standard Immunosuppression for Aplastic Anemia. N Engl J Med 2017;376:1540-1550. 10. Scheinberg P, Rios O, Scheinberg P, Weinstein B, Wu CO, Young NS. Prolonged cyclosporine administration after antithymocyte globulin delays but does not prevent relapse in severe aplastic anemia. Am J Hematol 2014;89(6):571-574. 11. Socie G, Rosenfeld S, Frickhofen N, Gluckman E, Tichelli A. Late clonal diseases of treated aplastic anemia. Semin Hematol 2000 Jan;37(1):91-101. 12. Champlin R, Ho W, Gale RP. Antithymocyte globulin treatment in patients with aplastic anemia: a prospective randomized trial. N Engl J Med 1983;308:113-118. 13. Frickhofen N, Kaltwasser JP, Schrezenmeier H, Raghavachar A, Vogt HG, Herrmann F, et al. Treatment of aplastic anemia with antilymphocyte globulin and methylprednisolone with or without cyclosporine. The German Aplastic Anemia Study Group. N Engl J Med 1991 May 9;324(19):1297-1304. 14. Babushok DV, Perdigones N, Perin JC, Olson TS, Ye W, Roth JJ, et al. Emergence of clonal hematopoiesis in the majority of patients with acquired aplastic anemia. Cancer Genet 2015 Apr;208(4):115-128. 15. Babushok DV, Xie HM, Roth JJ, Perdigones N, Olson TS, Cockroft JD, et al. Single nucleotide polymorphism array analysis of bone marrow failure patients reveals characteristic patterns of genetic changes. Br J Haematol 2014;164:73-82. 16. Yoshizato T, Dumitriu B, Hosokawa K, Makishima H, Yoshida K, Townsley D, et al. Somatic Mutations and Clonal Hematopoiesis in Aplastic Anemia. N Engl J Med 2015;373:35-47. 17. Babushok DV, Olson TS, Bessler M. Somatic Mutations and Clonal Hematopoiesis in Aplastic Anemia. N Engl J Med 2015 Oct 22;373(17):1673. 18. Kulasekararaj AG, Jiang J, Smith AE, Mohamedali AM, Mian S, Gandhi S, et al. Somatic mutations

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identify a subgroup of aplastic anemia patients who progress to myelodysplastic syndrome. Blood 2014 Oct 23;124(17):2698-2704. 19. Afable II MG, Wlodarski M, Makishima H, Shaik M, Sekeres MA, Tiu RV, et al. SNP array-based karyotyping: differences and similarities between aplastic anemia and hypocellular myelodysplastic syndromes. Blood 2011;117:6876-6884. 20. Young NS, Kaufman DW. The epidemiology of acquired aplastic anemia. Haematologica 2008 Apr;93(4):489-492. 21. Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR. Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations. Nucleic Acids Res 2011 Jan;39:D913-919. 22. Kekre N, Zhang Y, Zhang MJ, Carreras J, Ahmed P, Anderlini P, et al. Effect of antithymocyte globulin source on outcomes of bone marrow transplantation for severe aplastic anemia. Haematologica 2017 July;102(7):1291-1298. 23. Peinemann F, Grouven U, Kroger N, Pittler M, Zschorlich B, Lange S. Unrelated donor stem cell transplantation in acquired severe aplastic anemia: a systematic review. Haematologica 2009 Dec;94(12):1732-1742. 24. Cesaro S, Peffault de Latour R, Tridello G, Pillon M, Carlson K, Fagioli F, et al. Second allogeneic stem cell transplant for aplastic anaemia: a retrospective study by the Severe Aplastic Anaemia Working Party of the European Society for Blood and Marrow Transplantation. Br J Haematol 2015 Nov;171(4):606-614.

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Table 1. HLA-B*14:02 and HLA*B-40:02 are enriched in AA patients HLA-B*14:02 HLA-B*40:02

Population Subjects (n) Present, n (%) Absent, n (%) P-value Present, n (%) Absent, n (%) P-value

Control 1242890 71093 (5.7%) 1171797 (94.3%)

31321 (2.5%) 1211569 (97.5%)

AA 42 12 (28.6%) 30 (71.4%) 0.000 4 (9.5%) 38 (90.5%) 0.021

MDS 163 13 (8.0%) 150 (92.0%) 0.233 4 (2.5%) 159 (97.5%) 1.000 P-values refer to comparisons between patients with AA or MDS, as compared to the race-matched USA NMDP European Caucasian control population. Statistically significant P-values are shown in bold.

Table 2. Characteristics of SAA patients

HLA-identical

siblings 8/8 Matched

Unrelated Variable N (%) N (%) Number of Recipients 3015 840 Number of centers 311 171 Patient-related Recipient age at transplant

0-9 487 (16) 128 (15) 10-19 951 (32) 221 (26) 20-29 693 (23) 168 (20) 30-39 359 (12) 100 (12) 40-49 264 ( 9) 65 ( 8) 50-59 181 ( 6) 81 (10) 60+ 80 ( 3) 77 ( 9) Median (range) 21 (0-77) 23 (0-77)

Recipient sex Male 1768 (59) 441 (53) Female 1247 (41) 399 (48)

Disease-related Subdisease at transplant

Severe aplastic anemia unknown 72 ( 2) 2 (<1) SAA idiopathic 2706 (90) 706 (84) SAA secondary to hepatitis 122 ( 4) 41 ( 5) SAA secondary to toxin-other 40 ( 1) 22 ( 3) Amegakaryocytosis (not congenital) 3 (<1) 4 (<1) Acquired pure red cell aplasia 30 ( 1) 7 ( 1) Dyskeratosis congenital 2 (<1) 17 ( 2) Other acquired cytopenic syndrome, specify 40 ( 1) 41 ( 5)


Bone marrow 2104 (70) 644 (77) Peripheral blood 827 (27) 196 (23) BM+PBSC 84 ( 3) 0

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HLA-identical


Unrelated Variable N (%) N (%) GvHD prophylaxis

No GVHD prophylaxis (forms under review) 55 ( 2) 16 ( 2) Ex vivo T-cell depletion 14 (<1) 10 ( 1) CD34 selection 19 ( 1) 15 ( 2) Post-transplant Cy + others 29 ( 1) 9 ( 1) Tacrolimus + MMF ± others 89 ( 3) 90 (11) Tacrolimus + MTX ± others (except MMF) 378 (13) 278 (33) Tacrolimus + others (except MTX, MMF) 33 ( 1) 32 ( 4) Tacrolimus alone 46 ( 2) 37 ( 4) CSA + MMF ± others (except Tacrolimus) 145 ( 5) 56 ( 7) CSA + MTX ± others (except Tacrolimus, MMF) 1624 (57) 244 (29) CSA + others (except Tacrolimus, MTX, MMF) 48 ( 2) 21 ( 3) CSA alone 299 (11) 19 ( 2) Other GVHD prophylaxis 61 ( 2) 8 ( 1) Unknown 175 (N/A) 5 (N/A)

Conditioning regimen Myeloablative 200 ( 7) 75 ( 9) RIC 37 ( 1) 46 ( 5) Nonmyeloablative 269 ( 9) 245 (29) Other 2509 (83) 474 (57)

Donor / recipient sex matching Donor male / recipient male 6 (<1) 318 (38) Donor Male/ recipient female 4 (<1) 249 (30) Donor female/ recipient male 2 (<1) 96 (11) Donor female/ recipient female 0 129 (15) To be determined / Missing 3003 (>99) 48 ( 6)

Donor / recipient CMV serostatus Negative / negative 4 (<1) 218 (26) Negative / positive 2 (<1) 253 (30) Positive / negative 1 (<1) 70 ( 8) Positive / positive 3 (<1) 234 (28) Unknown 3005 (>99) 65 ( 8)

Donor age at donation To be determined / missing 2195 (73) 698 (83) 0-9 144 ( 5) 0 10-19 254 ( 8) 4 (<1) 20-29 183 ( 6) 58 ( 7) 30-39 100 ( 3) 46 ( 5) 40-49 75 ( 2) 28 ( 3) 50+ 64 ( 2) 6 ( 1) Median (range) 20 (0-73) 32 (19-60)

Year of transplant 2005 288 (10) 30 ( 4)

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HLA-identical


Unrelated Variable N (%) N (%)

2006 317 (11) 56 ( 7) 2007 225 ( 7) 37 ( 4) 2008 263 ( 9) 73 ( 9) 2009 279 ( 9) 58 ( 7) 2010 285 ( 9) 53 ( 6) 2011 278 ( 9) 81 (10) 2012 252 ( 8) 67 ( 8) 2013 221 ( 7) 100 (12) 2014 193 ( 6) 96 (11) 2015 207 ( 7) 101 (12) 2016 207 ( 7) 88 (10)


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Proposal 1711-06

Title: Role of HLA Allotypes in Determining CMV and Leukemia Specific Outcomes in Patients Undergoing Unrelated Donor Allogeneic Hematopoietic Cell Transplantation

Brian Shaffer: Memorial Sloan Kettering Cancer Center Rosa Sottile: Memorial Sloan Kettering Cancer Center Richard O’Reilly: Memorial Sloan Kettering Cancer Center Katharine C. Hsu: Memorial Sloan Kettering Cancer Center

Hypothesis: We propose that specific HLA allotypes induce a weak T-cell effector cell response against CMV, leading to an increased likelihood of CMV reactivation and greater incidence of CMV-specific complications after allogeneic hematopoietic cell transplantation (allo HCT). CMV reactivation associates with protection from leukemia relapse in some datasets. A secondary hypothesis of this study is that the benefit of CMV infection on relapse prevention is restricted to donor/recipient pairings with HLA allotypes that result in weak CMV specific donor T-cell responses.

Specific aims: Primary endpoint: Determine the incidence of cytomegalovirus reactivation in patients undergoing allo HCT from donors with HLA haplotypes that predict for a weak vs robust anti-CMV T-cell response.

The secondary endpoints: • To determine the incidence of relapse in patients with CMV-avid versus CMV-weak HLA

allotypes.• To determine 1- and 3-year overall survival, incidence of grade II-IV acute GVHD, incidence

of chronic GVHD, non-relapse mortality, and disease free survival between the two groups.• To determine the impact of CMV-avid versus CMV-weak HLA allotypes on donor and

recipient CMV serostatus

Scientific impact: Confirmation that HLA restriction is associated with risk for CMV reactivation could have immediate predictive impact for HCT patients and their risk for CMV disease, transplant-related mortality, and potentially disease relapse. Insights from this study are highly relevant to NK and T-cell biology and human CMV control. This study may only be attempted using a large scale registry such as the CIBMTR. Both positive negative results from this inquiry are informative.

Scientific justification: Reactivation of CMV is a major complication in allo HCT recipients, occurring in 60-90% of CMV seropositive recipients [1]. Complications from CMV infection after allogeneic HCT increase the likelihood of early death following transplantation. Primary CMV infection leaves a significant and durable imprint on effector cell populations including expansion of CMV specific memory T-cell and adaptive NK cell populations in healthy individuals[2-3], as well as in transplant recipients[4,5]. Control of CMV infection occurs primarily via the expansion of T-cells bearing antigen-specific T-cell receptors. Other lymphocyte populations, particularly NK cells, also participate in CMV control.

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Studies in healthy donors demonstrate that the relative impact of CMV on the T-cell repertoire varies based on the HLA allotype[3]. Here, specific HLA allotypes will result in relatively greater or weaker usage of TCR sequences implicated in the anti-CMV response. In both healthy donors and renal allograft recipients a relative expansion of TCR sequences occurs that are restricted to HLA-A*02, B*07 and to a lesser degree B*08 and a relative paucity of TCR rearrangements interact with antigens presented by HLA-B*44 [6-8]. These data suggest that certain HLA allotypes are therefore more apt to trigger adaptive immune responses and others will confer a relatively dampened response. In a small cohort of HCT patients, the frequencies of HLA-B*35-restricted CD8+ T- cells were found to be significantly lower than HLA-A2-restricted CD8+ T- cells, suggesting differential contributions of these cells to the control of CMV[9]. We have tested the hypothesis that CMV-specific complications vary based on donor HLA allotypes and confirmed these observations in allogeneic HCT recipients treated at our center (unpublished data). Several studies have identified an association between CMV reactivation and reduced incidence of relapse in patients with acute leukemia undergoing allogeneic HCT[10,11]. The mechanism for this relationship is unclear. Subsequent publications including an analysis conducted by the CIBMTR failed to confirm a relationship between CMV reactivation and relapse[12]. One possibility for these disparate results is the relative contribution of donor HLA allotypes in shaping the immune response to CMV infection after allogeneic HCT. CMV infection also promotes expansion of NKG2C+ “adaptive” NK cell populations that have demonstrated enhanced anti-leukemia cytotoxicity in vitro[13,14]. In healthy immunocompetent individuals the CMV infection usually goes unnoticed but some rare cases of symptomatic primary CMV infection have been reported[15]. It has been recently shown that healthy immunocompetent Individuals carrying an HLA-Bw4T allele are more susceptible to develop symptomatic CMV infection[16]. Little is known about the presence of a particular HLA allotype and the CMV serostatus in the general population. In the proposed study we will examine CMV-specific outcomes in transplant recipients stratified by HLA allotype. This analysis is based on pre-clinical and clinical data that implicate specific class 1 HLA allele groups in calibrating donor response to CMV. We will also examine the CMV serostatus of patients pretransplant and donors stratified by HLA allotype. Study population: This study will include two cohorts: (1) adult (age >18) recipients of HLA matched unrelated adult donor allo HCT with a diagnosis of AML, ALL, CML, and MDS from 2003 to present. Other patient eligibility criteria will mirror IN12-01. Patients much have complete HLA class 1 and HLA-DR typing and core clinical outcomes data available for inclusion. Unlike IN12-01, the UCB recipients will be excluded. (2) All donors with evaluable class 1 HLA allotypes and CMV serology available in the NMDP. Data requirements: The proposed study will mirror the eligibility criteria from IN12-01 and could use the compiled database from 2003-2010 used for this study to minimize statistician time. The major updates to this dataset would require: (1) the addition of intermediate or high resolution HLA typing of HLA-A, HLA-B, HLA-C, and HLA-DR to subjects included in IN12-01, (2) the inclusion of patients undergoing transplantation from 2010-2015 with the same data collection parameters, (3) the removal of cord blood transplants. Sample requirements: The study does not require the use of biological specimens.

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Study design: To address the first primary endpoint subjects will be stratified into the following groups based on intermediate or greater HLA typing. If ambiguous alleles are defined the most common allele type will be substituted if the frequency of the minor allele(s) are <5%. To assess for feasibility we examined the frequency of HLA allotypes among 1,729 donors who for patients with AML undergoing adult unrelated donor allo HCT facilitated by the NMDP. The frequency of the proposed analysis groups are below:

Donor HLA Allotype Frequency (%) Classification HLA-A*02:01 48.1% Collectively defined as

“CMV-avid” allotypes HLA-B*07:02 26.3% HLA-B*08:01 23.1% HLA-B*35 33.3% Collectively defined as

“CMV-weak” allotypes HLA-B*44 25.0% HLA-A*02:01 without CMV- weak specific allotypes

22.6%

HLA-B*07:02 without CMV- weak allotypes

16.0%

HLA-B*08:01 without CMV- weak allotypes

15.2%

HLA-B*35 without CMV-avid allotypes

7.0%

HLA-B*44:01 without CMV- avid allotypes

7.3%

Incidence of the primary endpoint of CMV reactivation will be tested in a univariate analysis as a time-dependent variable, considering death from other causes as a competing risk in the above groups. If similar frequencies of reactivation are seen in the CMV-avid HLA allotypes as well as between the CMV-weak allotype subgroups these may be combined into “CMV-avid” and “CMV- weak” groups for comparison. Survival outcomes will be determined with Kaplan Meier analysis. Relapse and GVHD endpoints will be examined with Cox regression analysis considering death from other causes as a competing risk. Covariates to consider will include the use of T-cell depleting antibodies such as ATG or Campath, GVHD prophylaxis regimens, conditioning intensity, donor graft source (BM v PB), and recipient and donor age. Association between HLA allotype and CMV serostatus will be assessed in a univariate analysis considering all the donors who voluntarily signed up for donation. Data source: This study will use the CIBMTR Research Database. References: 1. Ljungman P, Hakki M, Boeckh M. Cytomegalovirus in Hematopoietic Stem Cell Transplant Recipients. Hematol Oncol Clin North Am 2011 Feb;25(1):151-169. 2. Béziat V, Liu LL, Malmberg JA, et al. NK cell responses to cytomegalovirus infection lead to stable imprints in the human KIR repertoire and involve activating KIRs. Blood 2013 Apr 4;121(14):2678-2688.

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3. Emerson RO, DeWitt WS, Vignali M, et al. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat Genet 2017 May;49(5):659-665. 4. Davis ZB, Cooley, SA, Cichocki, et al. Adaptive Natural Killer Cell and Killer Cell Immunoglobulin Like Receptor Expressing T Cell Responses are Induced by Cytomegalovirus and Are Associated with Protection against Cytomegalovirus Reactivation after Allogeneic Donor Hematopoietic Cell Transplantation. Biol Blood Marrow Transplant 2015 Sep;21(9):1653-1662. 5. Suessmuth Y, Mukherjee R, Watkins B, et al. CMV reactivation drives posttransplant T-cell reconstitution and results in defects in the underlying TCRbeta repertoire. Blood 2015 June 18;125(25):3835-50. 6. Lacey SF, Villacres MC, La Rosa C, et al. Relative dominance of HLA-B*07 restricted CD8+ T-lymphocyte immune responses to human cytomegalovirus pp65 in persons sharing HLA-A*02 and HLA-B*07 alleles. Hum Immunol 2003;64(4):440-52. 7. Yang X, Gao M, Chen G, et al. Structural Basis for Clonal Diversity of the Public T Cell Response to a Dominant Human Cytomegalovirus Epitope. J Biol Chem 2015 Nov 27;290(48):29106-19. 8. Futohi F, Saber A, Nemati E, et al. Human Leukocyte Antigen Alleles and Cytomegalovirus Infection After Renal Transplantation. Nephrourol Mon 2015 Nov;7(6):e31635. 9. Giest S, Grace S, Senegaglia AC, et al. Cytomegalovirus-specific CD8(+) T cells targeting different HLA/peptide combinations correlate with protection but at different threshold frequencies. Br J Haematol 2010;148(2):311-22. 10. Green ML, Leisenring WM, Xie H, et al. CMV reactivation after allogeneic HCT and relapse risk: evidence for early protection in acute myeloid leukemia. Blood 2013 Aug 15;122(7):1316-24. 11. Takenaka K, Asano-Mori Y, Oshima K, et al. Cytomegalovirus Reactivation after Allogeneic Hematopoietic Stem Cell Transplantation is Associated with a Reduced Risk of Relapse in Patients with Acute Myeloid Leukemia Who Survived to Day 100 after Transplantation: The Japan Society for Hematopoietic Cell Transplantation Transplantation-related Complication Working Group. Biol Blood Marrow Transplant 2015 Nov;21(11):2008-16. 12. Teira P, Battiwalla M, Ramanathan M, et al. Early cytomegalovirus reactivation remains associated with increased transplant-related mortality in the current era: a CIBMTR analysis. Blood 2016 May 19;127(20):2427-38. 13. Redondo-Pachón D, Crespo Marta, Yelamos J, et al. Adaptive NKG2C+ NK Cell Response and the Risk of Cytomegalovirus Infection in Kidney Transplant Recipients. J Immunol 2016. 14. Liu LL, Beziat V, Oei VYS, et al. Ex Vivo Expanded Adaptive NK Cells Effectively Kill Primary Acute Lymphoblastic Leukemia Cells. Cancer Immunol Res. 2017 Aug;5(8):654-665. 15. Riou R, Bressollette-Bodin C, Boutoille D, et al. Severe Symptomatic Primary Human Cytomegalovirus Infection despite Effective Innate and Adaptive Immune Responses. J Virol 2017 Mar 1;91(5): e02245-16. 16. Di Bona D, Scafidi V, Plaia A, et al. HLA and killer cell immunoglobulin-like receptors influence the natural course of CMV infection. J Infect Dis 2014;210(7):1083-9.

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Table 1. Characteristics of AML/ALL/CML/MDS adult patients that are 8/8 HLA-matched with unrelated donors

Variable N (%) Number of recipients 3560 Number of centers 132 Patient-related Recipient age at transplant

18-19 40 ( 1) 20-29 295 ( 8) 30-39 352 (10) 40-49 507 (14) 50-59 848 (24) 60 years and older 1518 (43) Median (range) 57 (18-82)

Recipient sex Male 2041 (57) Female 1519 (43)

URD HLA-matching 8/8 allele matched for HLA-A, -B, -C and –DRB1 3560 (100)


AML 1757 (49) ALL 401 (11) CML 183 ( 5) MDS 1219 (34)

Disease status at transplant Early 1589 (45) Intermediate 418 (12) Advanced 1112 (31) Other 441 (12)


Bone marrow 591 (17) Peripheral blood 2969 (83)

GvHD Prophylaxis No GVHD prophylaxis (forms under review) 57 ( 2) Ex vivo T-cell depletion 9 (<1) CD34 selection 41 ( 1) Post-TX Cy + others 91 ( 3) Tacrolimus + MMF ± others 659 (19) Tacrolimus + MTX ± others (except MMF) 1970 (55) Tacrolimus + others (except MTX, MMF) 244 ( 7) Tacrolimus alone 87 ( 2) CSA + MMF ± others (except Tacrolimus) 191 ( 5) CSA + MTX ± others (except Tacrolimus, MMF) 129 ( 4)

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Variable N (%) CSA + others (except Tacrolimus, MTX, MMF) 15 (<1) CSA alone 18 ( 1) Other GVHD prophylaxis 49 ( 1)

Conditioning regimen Myeloablative 2035 (57) RIC 1246 (35) Nonmyeloablative 86 ( 2) Other 193 ( 6)

Donor / recipient sex matching Male / male 1571 (44) Male / female 1017 (29) Female / male 470 (13) Female / female 502 (14)

Donor / recipient CMV serostatus Negative / negative 1052 (30) Negative / positive 1254 (35) Positive / negative 369 (10) Positive / positive 885 (25)

Donor age at donation To be determined 3540 (99) 0-9 1 (<1) 20-29 9 (<1) 30-39 4 (<1) 40-49 3 (<1) 50+ 3 (<1) Median (range) 30 (0-55)

Year of transplant 2007 30 ( 1) 2008 444 (12) 2009 453 (13) 2010 317 ( 9) 2011 255 ( 7) 2012 249 ( 7) 2013 517 (15) 2014 599 (17) 2015 547 (15) 2016 149 ( 4)

Follow-up among survivors, months N Eval 1631 Median (range) 37 (1-120)

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Proposal 1711-106

Title: The Impact Of MHC Class I Chain-Related Gene A (MICA) 129 Polymorphism On CMV infection in Unrelated Donor Hematopoietic Cell Transplants (HCT) For Hematological Malignancies – Extension of Study IB13-05

Medhat Askar: Baylor University Medical Center Ronald Sobecks: Cleveland Clinic

Hypothesis: The non-classical major histocompatibility complex (MHC) class I chain–related A (MICA) polymorphism influences the risk of CMV infection after unrelated donor hematopoietic cell transplants (HCT) for hematological malignancies.

Specific aim: To investigate the association between MICA position-129 polymorphism (as a surrogate marker of NK cell activation through its cognate receptor NKG2D) of HCT recipients and donors and CMV infection after unrelated donor HCT.

Scientific justification: Cytomegalovirus (CMV) infection is a major cause of morbidity and mortality after HCT. In vitro, CMV infection strongly induces expression of the MICA 1. MICA is a ligand for the activating natural killer group 2, member D (NKG2D) receptor, which is expressed on the surface of natural killer (NK), NKT, CD8, and TCRGδ T cells2. The association between NKG2D and DAP10 adaptor molecule is required for NKG2D cell surface expression and ligand mediated activation of NK cells via a Src-PI3 kinase and Grb2-Vav signaling pathways that results in cytotoxicity and cytokine production3. Allelic variants of MICA have been reported to exhibit large differences in binding affinity to NKG2D4. These MICA alleles are defined by a dimorphism of a single nucleotide polymorphism (rs1051792 A > G) at position 454 in the third exon of the MICA gene, corresponding to amino acid 129 in the alpha-2 domain of the MICA protein5. MICA alleles with a methionine (M) or valine (V) have been classified as having strong or weak binding affinity for NKG2D, respectively. The MICA met has greater affinity (10- to 50-fold) for the NKG2D receptor than the MICA-129 val allele which may affect thresholds of NK cell triggering and T cell modulation4. These variable affinities have been suggested to affect thresholds of NK cell triggering and T cell modulation and, consequently, influence clinical phenotypes in autoimmune disorders and malignancies6,7. Recipient homozygosity of V alleles (VV) was reported by Boukouaci and colleagues to be associated with chronic GVHD in a study that primarily included myeloablative transplants8. In that study recipient MICA-129 VV genotype was found to increase the risk of chronic GvHD in a multivariable model (HR, 1.61; 95% CI, 1.08-2.40; p =0.019). The authors hypothesized that the weak engagement of NKG2D receptors by the weak binder MICA-129 V allele may impair NK/cytotoxic T lymphocyte cell activation/costimulation, possibly skewing the TH1 pathway toward TH2 with consequent B-cell activation and antibody production which are two hallmarks of chronic GvHD pathogenesis9. A recent single center study has shown that in multivariable analysis, MICA-129 V/V was associated with increased risk of CMV infection (HR 1.40, CI 1.00-1.96, P=0.05) (Figure 1) [Patel et al, ASH 2017 ORAL Abstract].

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Figure 1: Incidence of CMV infection after unrelated donor HCT by MICA-129 M/V dimorphism Study population: The same cohort of previous CIBMTR study # IB13-05 published in BBMT10. This cohort included 10/10 and 9/10 HLA matched adult HSCT recipients with ALL, AML, or MDS with known HLA-DPB1, typing and available DNA or equivalent samples to allow for MICA genotyping of recipients and their corresponding donors. Since it has been suggested that transplants matched for DPB1 may have higher prevalence of donors and recipients that match at the full haplotype level, matching at loci DRB3/4/5, DPA1, DQA1 will be adjusted for when known. Data requirements: All data already collected previously and included:

• The primary outcomes to be analyzed are: − Acute GvHD: all grades, severe (III & IV), and GI − Chronic GvHD: All grades and extensive

• Secondary outcomes (if available): Relapse, transplant related mortality, disease free survival, overall survival, failure of engraftment, time to complete donor chimerism, platelet and neutrophil engraftment, and CMV reactivation.

Variables to be analyzed: Patient-related:

• Age • Gender • Race • CMV status

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Disease-related: Diagnosis (ALL, AML vs. MDS) Disease Stage

Transplant-related: • HLA matching: 10/10 vs. /10 and matching grade at loci DRB3/4/5, DQA1, DPA1, and DPB1 • Year of transplant • Donor age • Donor race • Donor gender • Donor CMV status • Conditioning regimen • Graft manipulation (TCD will be excluded) • GVHD prophylaxis (ATG & Campath will be excluded) • ABO matching • Time from diagnosis to transplant

Sample requirements: No further testing is required since this cohort of recipients and donors already typed for MICA Study design: Retrospective analysis of the association between MICA position-129 polymorphism of HCT recipients and donors and CMV infection after unrelated donor HCT in this study cohort. Regression analysis will be used to identify risk factors for CMV infection. The analysis will be performed examining dimorphisms at the MICA-129 position, which previously has been categorized as weaker (valine/valine: V/V), heterozygous (methionine/valine: M/V), or stronger (methionine/methionine: M/M) receptor binding affinity. 1. Breakdown into three groups from the previous analysis: MM MV VV Recipients 101 (14%) 363 (52%) 239 (34%) Donors 106 (15%) 375 (53%) 229 (32%) Experience of the Investigator in the proposed methods: The first investigator is an MD with a PhD in Microbiology and Immunology and is certified by the American Board of Histocompatibility & Immunogenetics (ABHI), the American Board of Medical Laboratory Immunology (ABMLI), and the American Board of Bioanalysis as High Complexity Laboratory Director (ABB, HCLD). He is the director of a high volume full service histocompatibility and immunogentics laboratory that supports all transplant programs of a large academic institute (Baylor University Medical Center) including kidney, pancreas, liver, small bowel, heart, lung, multi-organ, face, and HCT including cord blood transplants. The results of the previous study on the same cohort has been published 10. Our group has previously identified 2 new MICA alleles and published a number of abstracts and presented numerous presentations that involve MICA genotyping.

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References: 1. Groh V, Rhinehart R, Randolph-Habecker J, Topp MS, Riddell SR, Spies T. Costimulation of CD8alphabeta T cells by NKG2D via engagement by MIC induced on virus-infected cells. Nat Immunol 2001;2(3):255-260. 2. Nausch N, Cerwenka A. NKG2D ligands in tumor immunity. Oncogene 2008;27(45):5944-5958. 3. Champsaur M, Lanier LL. Effect of NKG2D ligand expression on host immune responses. Immunol Rev 2010;235(1):267-285. 4. Steinle A, Li P, Morris DL, et al. Interactions of human NKG2D with its ligands MICA, MICB, and homologs of the mouse RAE-1 protein family. Immunogenetics 2001;53(4):279-287. 5. Stephens HA. MICA and MICB genes: can the enigma of their polymorphism be resolved? Trends Immunol 2001;22(7):378-385. 6. Douik H, Ben Chaaben A, Attia Romdhane N, et al. Association of MICA-129 polymorphism with nasopharyngeal cancer risk in a Tunisian population. Hum Immunol 2009;70(1):45-48. 7. Amroun H, Djoudi H, Busson M, et al. Early-onset ankylosing spondylitis is associated with a functional MICA polymorphism. Hum Immunol 2005;66(10):1057-1061. 8. Boukouaci W, Busson M, Peffault de Latour R, et al. MICA-129 genotype, soluble MICA, and anti-MICA antibodies as biomarkers of chronic graft-versus-host disease. Blood 2009;114(25):5216-5224. 9. Imanguli MM, Swaim WD, League SC, Gress RE, Pavletic SZ, Hakim FT. Increased T-bet+ cytotoxic effectors and type I interferon-mediated processes in chronic graft-versus-host disease of the oral mucosa. Blood 2009;113(15):3620-3630. 10. Askar M, Sobecks R, Wang T, et al. MHC Class I Chain-Related Gene A (MICA) Donor-Recipient Mismatches and MICA-129 Polymorphism in Unrelated Donor Hematopoietic Cell Transplantations Has No Impact on Outcomes in Acute Lymphoblastic Leukemia, Acute Myeloid Leukemia, or Myelodysplastic Syndrome: A Center for International Blood and Marrow Transplant Research Study. Biol Blood Marrow Transplant 2017;23(3):436-444.

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Table 1. Selected cases showing characteristics of recipients receiving first allogeneic unrelated donor transplants for AML, ALL or MDS with high-resolution HLA typing for HLA-A, -B, -C, –DRB1 and -DQB1

and have samples available for donor and recipient from 2000-2011a 9/10 10/10 Variable N(%) N(%) P-value Number of patients 163 555 Number of centers 57 99 Recipient age at transplant, median (range), years 44 (18-74) 50 (18-74) 0.001

18-19 9 ( 6) 11 ( 2) 0.04 20-29 34 (21) 87 (16) 30-39 20 (12) 64 (12) 40-49 38 (23) 108 (19) 50-59 35 (21) 171 (31) 60-69 25 (15) 105 (19) 70+ 2 ( 1) 9 ( 2)

Recipient race / ethnicity < 0.0001 Caucasian, Non-Hispanic 130 (83) 524 (96) African American, Non-Hispanic 6 ( 4) 4 ( 1) Asian, Non-Hispanic 4 ( 3) 3 ( 1) Native American, Non-Hispanic 0 2 (<1) Hispanic, Caucasian race 16 (10) 13 ( 2) Hispanic, African American race 0 1 (<1) Hispanic, Unknown race 1 ( 1) 1 (<1) Other 6 7

Male sex 85 (52) 295 (53) 0.82 Karnofsky prior to transplant > 90 95 (63) 332 (65) 0.66 Disease at transplant 0.08

AML 79 (48) 320 (58) ALL 33 (20) 80 (14) MDS 51 (31) 155 (28)

Disease stage at transplant 0.56 Early 66 (40) 393 (71) Intermediate 34 (21) 5 ( 1) Advanced / late 47 (29) 138 (25) Other 16 (10) 19 ( 3)

Stem cell source 0.70 Bone marrow 39 (24) 141 (25) Peripheral blood 124 (76) 414 (75)

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9/10 10/10 Variable N (%) N (%) P-value Conditioning regimen 0.47

Myeloablative TBI > 500 cGy single dose / > 800 fractionated

60 (37)

167 (31)

Bu oral > 9 mg/Kg (Bu IV > 7.2) + Cyclophosphamide

31 (19) 112

(21)

Bu oral > 9 mg/Kg (Bu IV > 7.2), No Cyclophosphamide

30 (18) 102 (19)

Melphalan > 150 mg/m2 1 ( 1) 0 Reduced intensity

TBI < 500 cGy single dose / < 800 fractionated

1 ( 1) 7 ( 1)

Melphalan ≤ 150 mg/m2 12 ( 7) 48 ( 9) Bu oral ≤ 9 mg/Kg, (Bu IV ≤ 7.2) 14 ( 9) 69 (13)

Nonmyeloablative TBI ≤ 200 cGy, No Fludarabine 0 1 (<1) Fludarabine + TBI ≤ 200 cGy 4 ( 2) 21 ( 4) Fludarabine + Cyclophosphamide 3 ( 2) 9 ( 2) Other 7 ( 4) 19 ( 4)

GVHD prophylaxis 0.32 Tacrolimus + MMF ± others 23 (14) 99 (18) Tacrolimus + MTX ± others (except MMF) 98 (60) 327 (59) CSA + MMF ± others (except Tacrolimus) 10 ( 6) 46 ( 8) CSA + MTX ± others (except Tacrolimus,

MMF) 32 (20) 83 (15)

In vivo T-Cell depleted 59 (36) 194 (35) 0.84 HLA-DPB1 matching 0.60

Allele matched 33 (28) 139 (33) Single mismatch 68 (58) 224 (54) Double mismatch 16 (14) 55 (13) Missing / to be determined 46 137

Donor / recipient sex match 0.57 Male / male 56 (34) 208 (37) Male / female 43 (26) 162 (29) Female / male 29 (18) 87 (16) Female / female 35 (21) 98 (18)

Donor / recipient CMV status 0.90 Negative / negative 43 (26) 160 (29) Negative / positive 59 (36) 185 (33) Positive / negative 16 (10) 60 (11) Positive / positive 42 (26) 138 (25) Unknown 3 ( 2) 12 ( 2)

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9/10 10/10 Variable N (%) N (%) P-valueDonor race/Ethnicity 0.0002

Caucasian, Non-Hispanic 122 (82) 474 (94) African American, Non-Hispanic 4 ( 3) 7 ( 1) Asian, Non-Hispanic 2 ( 1) 5 ( 1) Native American, Non-Hispanic 2 ( 1) 5 ( 1) Hispanic, Caucasian race 8 ( 5) 7 ( 1) Hispanic, Unknown race 10 ( 7) 7 ( 1) Other 1 ( 1) 1 (<1) Donor race/Ethnicity missing/To be determined

14 49

Donor age in years, median (range) 40 (19-60) 32 (18-61) < 0.0001 18-32 years old 46 (28) 290 (52) < 0.0001 33-49 years old 87 (53) 223 (40) 50 years old and older 28 (17) 33 ( 6) Donor age Missing/To be determined 2 ( 1) 9 ( 2)

Year of transplant 0.07 2000 5 ( 3) 7 ( 1) 2001 8 ( 5) 6 ( 1) 2002 5 ( 3) 17 ( 3) 2003 9 ( 6) 34 ( 6) 2004 17 (10) 36 ( 6) 2005 21 (13) 77 (14) 2006 19 (12) 78 (14) 2007 23 (14) 84 (15) 2008 24 (15) 68 (12) 2009 15 ( 9) 70 (13) 2010 11 ( 7) 48 ( 9) 2011 6 ( 4) 30 ( 5)

Median follow-up of survivors, month (range) 64 (12-119) 60 (3-147) a – Data has been CAP-modeled.

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Proposal 1711-79

Title: Evaluation of the impact of Donor KIR genotype on outcome after unrelated donor transplantation in patients with Myelodysplastic Syndromes or secondary Acute Myeloid Leukemia

Johannes Schetelig: EBMT Chronic Malignancies Working Party / TU Dresden / DKMS Nicolaus Kröger: President elect EBMT, Chronic Malignancies Working Party / UKE Hamburg Marie Robin: EBMT Chronic Malignancies Working Party / UKE Hamburg

Hypothesis: The hypothesis is that for patients with MDS or sAML donor genotype information on KIR3DL1, KIR2DS1 together with patient information on the respective KIR ligands (HLA-B and HLA-C alleles) allows prediction of the relapse incidence and event-free survival.

Specific aims: To study the impact of the grouping algorhythm for KIR3DL1 and its ligand Bw4 and KIR2DS1 with its ligand C2 as described by Jeannette Boudreau in JCO 2017 on relapse incidence and event-free survival1-

2. Exploratory analyses will be conducted to study the impact of donor KIR2DL1 and KIR2DS4 ontransplant outcomes in this population.

Scientific impact: Results of the study might lead to improved selection of unrelated donors for patients with MDS/ sAML.

Scientific justification: Evidence for the importance of the KIR3DL1/Bw4 and KIR2DS1/C2 receptor-ligand system on transplant outcomes comes from the group of Kathy Hsu at the MSKCC1-4. The classification approach integrates information on the expression level of different KIR3DL1 alleles and their binding affinity to the stronger or weaker Bw4 ligand caused by a dimorphism between isoleucine and threonine at position 80 in HLA-Bw4. Additional information on the clinical impact of this specific receptor ligand system comes from studies addressing the risk of transmission of HIV and HIV progression. Further evidence for this system comes from studies on the clearance of Hepatitis C virus5-7.

Study population: • Age >18 years• MDS (all subtypes, including sAML)• First allogeneic transplantation between 01/2013 and 03/2017• Transplantation from an Unrelated donor• Peripheral blood stem cells or bone marrow as graft source• Availability of Donor ID in the registry

Data requirements: • Donor-ID• Patient sex• Patient age at allo-HCT• Karnofsky performance status• Interval MDS diagnosis to allo-HCT• Previous autologous SCT

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• Previous pretreatment for MDS or for other malignancies/auto-immune disease • Highest MDS stage before allo-HCT (if available, according to WHO classification) • Disease risk, if available (if available, according to IPSS-R) • Karyotype, if available • Remission status at allo-HCT • Conditioning (NMA, RIC, MAC) • Use of ATG • Ex vivo or in vivo TCD • HLA match • HLA-C and HLA-B allele information • Recipient-donor sex match • Recipient-donor CMV match • Date of SCT • Time to last follow-up • Remission status at last follow-up • Date of relapse if applicable • Cause of death • Onset and grade AGVHD • Onset and grade CGVHD

Sample requirements: Donor samples and typing of the samples will be provided by the Collaborative Biobank (www.cobi-biobank.de) and the Life Science Laboratory of DKMS for free. Patient samples are not necessary and will not be requested. Study design: This study shall be a joint retrospective registry-based study of EBMT and CIBMTR. The Chronic Malignancies Working Party of EBMT has already approved the study. Medical data shall be taken from the databases of EBMT and the CIBMTR. Donor samples will be taken from the Collaborative Biobank. The donor samples will be genotyped for 14 KIR genes on allele level using an ultra-high-volume high resolution amplicon-based next generation sequencing method as described recently8. Exons 3, 4, 5, 7, 8, and 9 will be sequenced. In parallel HLA-typing of 6 loci (A, B, C, DRB1, DQB1, and DPB1) will be done for all donors on the HiSeq 2500 platform targeting the core HLA exons 2 and 3 as described previously9. The necessary sample size for this retrospective study has been estimated based on the Hazard Ratio for mortality of 0.84 (95%-confidence interval: 0.72 - 0.98, p=0.03) for the favorable (weak or non-inhibiting pairs) versus unfavorable (strong inhibiting pairs) receptor-ligand group as reported by Boudreau et al1-2. The primary endpoint for this study will be event-free survival. To confirm an effect of this size on EFS data from approximately 2000 patients have to be analyzed assuming overall 2-year event-free survival of 40% for the unfavorable group versus 46.5% for the favorable group (HR 0.84). For this calculation an allocation ratio of 1:3 (unfavorable : favorable matching), proportional hazards, 48 months recruitment period and 21 months of follow-up were assumed, together with test parameters alpha=5% and beta=20%. Integration of information on KIR2DS1 and its ligand and the categorization into four groups will increase the power of the analysis.

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Non-CIBMTR data source: The number of MDS patients who were registered with EBMT and have samples available in the Collaborative Biobank approximates 1800 patients. Based on feasibility checks, mapping of patients by the donor-ID will be successful for approximately 60%-70% donor recipient pairs, resulting in approximately 1200 patients that can be contributed by EBMT. In order to reach the critical number of 2000 patients we propose a joint EBMT-CIBMTR study and request access to data of patients who were registered with the CIBMTR and who have a donor sample available in the Collaborative Biobank. Donor and patient information in the CIBMTR shall be mapped via the donor-ID. Additional identity checks will comprise donor and patient sex as well as the chronological sequence of the date of stem cell donation (available at the Biobank) and the date of transplantation (available in the outcome registries). Finally, patients will be identified whose data were reported to EBMT and CIBMTR in order to rule out duplicates. References: 1. Boudreau JE, Giglio F, Gooley TA, et al. KIR3DL1/ HL A-B Subtypes Govern Acute Myelogenous Leukemia Relapse After Hematopoietic Cell Transplantation. J Clin Oncol 2017;35(20):2268-2278. 2. Venstrom JM, Pittari G, Gooley TA, et al. HLA-C-dependent prevention of leukemia relapse by donor activating KIR2DS1. N Engl J Med 2012;367(9):805-816. 3. Boudreau JE, Mulrooney TJ, Le Luduec JB, Barker E, Hsu KC. KIR3DL1 and HLA-B Density and Binding Calibrate NK Education and Response to HIV. J Immunol 2016;196(8):3398-3410. 4. Forlenza CJ, Boudreau JE, Zheng J, et al. KIR3DL1 Allelic Polymorphism and HLA-B Epitopes Modulate Response to Anti-GD2 Monoclonal Antibody in Patients With Neuroblastoma. J Clin Oncol 2016;34(21):2443-2451. 5. Thons C, Senff T, Hydes TJ, et al. HLA-Bw4 80(T) and multiple HLA-Bw4 copies combined with KIR3DL1 associate with spontaneous clearance of HCV infection in people who inject drugs. J Hepatol 2017;67(3):462-470. 6. Umemura T, Ota M, Katsuyama Y, et al. KIR3DL1-HLA-Bw4 combination and IL28B polymorphism predict response to Peg-IFN and ribavirin with and without telaprevir in chronic hepatitis C. Hum Immunol 2014;75(8):822-826. 7. Martin MP, Qi Y, Gao X, et al. Innate partnership of HLA-B and KIR3DL1 subtypes against HIV-1. Nat Genet 2007;39(6):733-740. 8. Schofl G, Lang K, Quenzel P, et al. 2.7 million samples genotyped for HLA by next generation sequencing: lessons learned. BMC Genomics 2017;18(1):161. 9. Lange V, Bohme I, Hofmann J, et al. Cost-efficient high-throughput HLA typing by MiSeq amplicon sequencing. BMC Genomics 2014;15:63.

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Table 1. Characteristics of MDS/AML patients for Proposal 1711-79, available through DKMS but not in IHWG cohort

Variable N (%) Number of recipients 585 Number of centers 115 Patient-related Recipient age at transplant

0-9 8 ( 1) 10-19 16 ( 3) 20-29 13 ( 2) 30-39 21 ( 4) 40-49 43 ( 7) 50-59 122 (21) 60+ 362 (62) Median (range) 63 (1-83)



AML 68 (12) MDS 517 (88)


Bone marrow 72 (12) Peripheral blood 511 (87) Umbilical cord blood 1 (<1) PBSC + UCB 1 (<1)

GvHD prophylaxis No GVHD prophylaxis (forms under review) 7 ( 1) Ex vivo T-cell depletion 1 (<1) CD34 selection 13 ( 2) Post-transplant Cy + others 21 ( 4) Tacrolimus + MMF ± others 91 (16) Tacrolimus + MTX ± others (except MMF) 319 (55) Tacrolimus + others (except MTX, MMF) 42 ( 7) Tacrolimus alone 16 ( 3) CSA + MMF ± others (except Tacrolimus) 33 ( 6) CSA + MTX ± others (except Tacrolimus, MMF) 25 ( 4) CSA + others (except Tacrolimus, MTX, MMF) 3 (1)

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Variable N (%) CSA alone 1 (<1) Other GVHD prophylaxis 8 ( 1) Unknown 5 (N/A)

Conditioning regimen Myeloablative 158 (27) RIC 210 (36) Nonmyeloablative 12 ( 2) Other 205 (35)

Donor / recipient sex matching Donor male / recipient male 272 (46) Donor male / recipient female 144 (25) Donor female / recipient male 83 (14) Donor female / recipient female 84 (14) Missing / To be determined 2 (<1)

Donor / recipient CMV serostatus Negative / negative 213 (36) Negative / positive 190 (32) Positive / negative 49 ( 8) Positive / positive 127 (22) Unknown 6 ( 1)

Donor age at donation To be determined /missing 578 (99) 20-29 3 ( 1) 30-39 1 (<1) 40-49 2 (<1) 50+ 1 (<1) Median (range) 36 (20-65)

Year of transplant 2011 1 (<1) 2012 1 (<1) 2013 57 (10) 2014 151 (26) 2015 157 (27) 2016 178 (30) 2017 40 ( 7)


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Proposal 1711-128

Title: Chromosomal Aberrations and Transplant Outcomes In Patients With Inherited Bone Marrow Failure Syndromes

Youjin Wang: Clinical Genetics Branch (CGB), Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute Shahinaz Gadalla: CGB, DCEG, National Cancer Institute

Collaborators: Sharon Savage: CGB, DCEG, National Cancer Institute Blanche Alter: CGB, DCEG, National Cancer Institute Meredith Yeager: Cancer Genomic Laboratory, DCEG Weiyin Zhou: Cancer Genomic Laboratory, DCEG

Hypothesis: We hypothesize that certain chromosomal aberrations in pre-hematopoietic cell transplantation (HCT) blood samples of patients with inherited bone marrow failure syndromes (IBMFS) are associated with worse outcomes after HCT.

Specific aims: • To describe the frequency and types of chromosomal aberrations in pre-HCT blood samples of

patients with IBMFS (Fanconi anemia (FA), Diamond-Blackfan anemia (DBA), Shwachman-Diamond syndrome (SDS), and dyskeratosis congenita (DC)), overall and by syndrome subtype.

• To evaluate the association between frequently detected chromosomal aberrations and post-HCT outcomes, overall and by syndrome subtype.

• To investigate whether the associations between chromosomal aberrations and post-HCToutcomes differ by transplant-related factors.

Scientific impact and justification: Allogeneic hematopoietic cell transplantation (HCT) is a curative option for hematologic manifestations in patients with IBMFS.1 Due to intrinsic susceptibility to certain complications and toxicities associated with the HCT procedure, IBMFS patients, particularly those with FA and DC, undergoing HCT are at a higher risk of post-transplant malignancies and treatment-related mortality.2 HLA typing improvements, changes in conditioning regimens and supportive care have significantly improved HCT outcomes in FA; 5-year overall survival after the year 2000 is approximately 76% for HLA-identical sibling HCT and 64%for unrelated donor transplants.3 Similar changes for DC are underway. Main causes of death in FA afterHCT are graft-versus-host disease (GvHD) and graft failure 4-6 and in DC are graft failure, pulmonarycomplications and infection.7,8

Patients with IBMFS are at high risk of clonal evolution to myelodysplastic syndrome (MDS) and/or acutemyeloid leukemia (AML).9 An analysis of the National Cancer Institute (NCI) IBMFS cohort after 15 yearsof follow-up showed that the cumulative incidence of MDS by age 50 was 50% in FA, 20% in DC, 65% inSDS and 5% in DBA.10 The cumulative incidence of leukemia was < 10% in DC by age 70, and < 5% in FAand SDS by age 30 and 20, respectively.10 A previous report showed a high frequency of cytogeneticclones in FA (16/33 patients; 48%); the clones were mainly in chromosome (chr) 1 (N=7), chr 7 (N=4), chr6 (N=3), and chr 13 (N=3).11 The same study found that FA patients with cytogenetic clones had worsesurvival than those without (5-year survival of 40% vs. 94%). Another study showed alterations in the

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form of gains (+) in chromosome 1q, 3q, 9p, and losses (-) in chromosomes 5q, 7q, 11q, and 20q.12 Association with poor prognosis was reported for FA patients with 3q+12-14, -7/7q-12,13, and those with complex cytogenetic abnormalities12 in multiple studies, and the presence of such alterations is suggested as an indication for prompt HCT in those patients.9,15 Data on the effects of chromosomal aberration in the setting of HCT for FA and other IBMFS are limited. Cytogenetic abnormalities are a major prognostic factor for HCT outcomes in hematologic malignancies, but their effect in IBMFS has not been well studied yet. One study analyzing 82 FA patients who received HLA-matched related HCT compared survival of patients with cytogenetic abnormalities only, MDS, or acute leukemia. The study showed that patients with pre-HCT cytogenetic abnormalities without MDS/AML diagnosis had better survival outcome than those with FA and MDS/AML (5-year survival 67% vs. 43%, p=0.03).16 The study did not compare outcomes in FA patients with and without cytogenetic abnormalities, didn’t specify types of chromosomal aberrations that affect HCT outcomes except for those in chromosome 1, and didn’t present data from patients who received unrelated donor HCT due to small sample size. Single-nucleotide polymorphism (SNP) arrays are high-resolution tools that capture genome-wide chromosomal aberrations17, many of which cannot be observed by conventional metaphase cytogenetics, such as unbalanced chromosomal gain or loss, and copy-number neutral loss of heterozygosity (CN-LOH).18,19 A previous analysis of 174 patients with MDS, secondary AML and myeloproliferative disease (MPD) showed that more patients were identified as having chromosomal lesions using SNP arrays than in metaphase cytogenetics; in MDS (78% vs. 59%), in MDS/MPD (75% vs. 37%) and in secondary AML (77% vs. 53%).17 CN-LOH was found in 20-35% of the patients, which was not detected in metaphase cytogenetic analyses. Higher numbers of chromosomal lesions identified with SNP arrays were also reported in a recent analysis of 104 MDS patients.20 We propose to use SNP-array analysis to detect pre-HCT chromosomal abnormalities in patients with IBMFS and evaluate the prognostic role of such alterations on HCT outcomes.

Preliminary Data: We generated SNP-array data for 73 FA patients who received unrelated HCT between 1991-2007. This analysis was part of the CIBMTR-NCI Transplant Outcome in Aplastic Anemia project (TOAA; CIBMTR proposal IB10-01). The study showed that 22% of the FA patients had pre-HCT clonal chromosomal aberrations (N=16). Frequently observed aberrations were chr7-/7p-/7q-, chr1q+ and chr3q+. Our HCT outcome analysis showed that the presence of chr1+ or chr3+ was associated with higher risks of post-transplant mortality (hazard ratio (HR)=2.69, 95% confidence interval=1.22-5.91, p=0.01). No post-HCT survival association was noted for chr7- (p=0.57). Of interest, the effect of such alterations differed by conditioning regimen; HRs in non-myeloablative/reduced intensity and myeloablative conditioning were 3.84 (p=0.02) and 1.47(p=0.53), respectively.

Study Rational and Importance: Our preliminary results suggest that certain clonal alterations in FA, and possibly other IBMFS, may be associated with worse HCT outcomes. Given the current significant improvement in transplant outcomes, we propose to validate our previous findings in a more recent cohort of FA patients, and to test if the presence of such alterations in other IBMFS patients may be associated with the same risk. We will evaluate whether the associations of chromosomal aberration differ by transplant-related factors, in an attempt to identify regimens suitable for those patients. Furthermore, the proposed study could provide evidence for a refined genomic risk stratification tool, which may assist clinical decision-making in IBMFS disease management.

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Study population: The study will include patients with an IBMFS (FA, DC, DBA, SDS) who received their first allogeneic HCT on or after January 2005, and who have a blood sample available in the CIBMTR biorepository. Scientific method: Genotyping: We will use the Illumina Infinium® Global Screening Array to generate SNP genotyping data. This will be conducted at the National Cancer Institute’s Cancer Genomics Research Laboratory. Chromosomal aberration detection: Log2 R ratio (LRR) and B allele frequency (BAF) will be used to detect and characterize chromosomal aberration events. LRR is log2 ratio of observed total signal intensities to expected signal intensities for a SNP, where >0 indicates copy number gain and < 0 indicates copy number loss. BAF is a ratio of signal intensity between A, B alleles at each SNP in relation to estimated genotype clusters from references. BAF value 0.5 indicates a heterozygous biallelic SNP, and a BAF value close to 0 or 1 indicates homozygous genotype. BAF changes when there are copy-number aberrations or copy-neutral changes from biparental to uniparental disomy (CN-LOH).21 LRR and BAF values will be normalized using the method described in a study by Jacobs et al.22 Corrected LRR and BAF data will then be analyzed with BAFSegmentation software package, which uses Circular Binary Segmentation (SBC) algorithm to detect chromosomal aberration events. The analysis will be restricted to events larger than 2 Mb in size to reduce the false-positive discoveries. Study outcomes: Primary outcomes

• Overall Survival Secondary outcomes:

• Engraftment • Acute GvHD (aGvHD) grades II-IV and grades III-IV • Chronic GvHD (cGvHD)


• Age at HCT, race, sex, Karnofsky score Disease-related:

• IBMFS subtype • Indication for transplant (SAA, MDS, and AML) • Pre-transplant clinical cytogenetics, if available

Transplant-related:

• Graft type, year of HCT, donor age, donor type (related, unrelated) • Donor/recipient sex match, donor/recipient cytomegalovirus serology status, • Conditioning regimen and intensity, GvHD prophylaxis regimen, degree of HLA matching, and

cause of death Sample requirements:

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Pre-transplant whole blood or PBMC samples are required. Patients with related and unrelated HCT are eligible for study inclusion.

Study design: Scientific plan: Aim 1: Identification and characterization of chromosomal aberrations in IBMFS patients by syndrome subtype We will describe frequencies, types (copy-gain, copy-loss, CN-LOH and complex alterations), and chromosome location of events by syndrome subtype. We will also assess the percentage of cells harboring such aberrations within each sample.

Aim 2: Evaluation of the association between post-HCT outcomes and chromosomal aberrations in patients with IBMFS by syndrome subtype We will evaluate the association between chromosomal aberrations (overall and by the most frequent) and primary/secondary outcomes using both univariate and multivariable analyses. Analyses will be stratified by syndrome subtype and combined if results were similar. The reference group for these analyses will be patients without chromosomal aberrations. Patient demographic and clinical factors will be evaluated for inclusion in the final model.

Aim 3: Evaluation of the association between post-HCT outcomes and chromosomal aberrations in patients with IBMFS, stratified by treatment related factors We will evaluate whether the associations differ by treatment-related factors such as intensity of conditioning regimen (when variations exist), use of radiation, and indication for transplant (SAA, MDS, and AML). Stratified univariate and multivariate analyses for each syndrome subtype, and combined will be conducted.

Statistical analysis: We will compare baseline characteristics between patients with chromosomal aberrations and those without using the chi-square or Fisher exact test for categorical variables, and the Student t-test or Mann-Whitney test for continuous variables, as appropriate.

We will use the Kaplan-Meier method to estimate survival probability at 1, 3 and 5 years for all and for prevalent chromosomal aberrations, separately, overall and by syndrome subtype. Log-rank test will be used for across-group comparisons. We will estimate the cumulative incidence of engraftment and GvHD; death from other causes will be considered a competing risk event. Gray’s test will be used for across-group comparisons.

For multivariable analysis, we will use Cox proportional hazard regression models. Proportional hazards assumptions will be evaluated for all the potential covariates using graphical methods and statistical tests. If violated, the models will be stratified on variables violating such assumptions. We will use the stepwise approach with a significance level of 0.1 for entry and 0.5 for staying in the model to determine potential variables in the final multivariable models. The hazard ratios and 95% confidence intervals of the outcomes-of-interest will be computed for all aberrations combined and for frequent events, overall and by syndrome subtype.

To test possible effect modifications, we will stratify the analyses by the intensity of the conditioning regimen (if applicable), use of radiation, and indication for transplant to examine whether the impact of chromosomal aberrations differs by those factors. We will compare the differences in risk estimates

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(hazard ratio) across categories using the Wald test. We will also test the potential interaction of chromosomal aberrations with these variables on primary and secondary outcomes. Power calculation The table below provides estimates for hazard ratios comparing patients with chromosomal aberrations and those without under different scenarios of aberration frequencies and survival probabilities in the reference group (patients without chromosomal abnormalities). The calculation assumption includes: type I error=0.05, power=80%, and a total sample size=150.

Proportion of patients with aberration

Survival probability in patients without aberration

Minimal detectable Hazard Ratio

0.10 0.60 2.32 0.70 2.53 0.80 2.90

0.15 0.60 2.12 0.70 2.31 0.80 2.63

0.20 0.60 2.03 0.70 2.20 0.80 2.50

References: 1. Peffault de Latour R, Peters C, Gibson B, et al. Recommendations on hematopoietic stem cell transplantation for inherited bone marrow failure syndromes. Bone Marrow Transplant 2015;50(9):1168-1172. 2. Dietz AC, Mehta PA, Vlachos A, et al. Current Knowledge and Priorities for Future Research in Late Effects after Hematopoietic Cell Transplantation for Inherited Bone Marrow Failure Syndromes: Consensus Statement from the Second Pediatric Blood and Marrow Transplant Consortium International Conference on Late Effects after Pediatric Hematopoietic Cell Transplantation. Biology of Blood and Marrow Transplantation 2017;23(5):726-735. 3. Peffault de Latour R, Porcher R, Dalle JH, et al. Allogeneic hematopoietic stem cell transplantation in Fanconi anemia: the European Group for Blood and Marrow Transplantation experience. Blood 2013;122(26):4279-4286. 4. Stepensky P, Shapira MY, Balashov D, et al. Bone marrow transplantation for Fanconi anemia using fludarabine-based conditioning. Biol Blood Marrow Transplant 2011;17(9):1282-1288. 5. Guardiola P, Pasquini R, Dokal I, et al. Outcome of 69 allogeneic stem cell transplantations for Fanconi anemia using HLA-matched unrelated donors: a study on behalf of the European Group for Blood and Marrow Transplantation. Blood 2000;95(2):422-429. 6. Wagner JE, Eapen M, MacMillan ML, et al. Unrelated donor bone marrow transplantation for the treatment of Fanconi anemia. Blood 2007;109(5):2256-2262. 7. Gadalla SM, Sales-Bonfim C, Carreras J, et al. Outcomes of allogeneic hematopoietic cell transplantation in patients with dyskeratosis congenita. Biol Blood Marrow Transplant 2013;19(8):1238-1243. 8. Barbaro P, Vedi A. Survival after Hematopoietic Stem Cell Transplant in Patients with Dyskeratosis Congenita: Systematic Review of the Literature. Biol Blood Marrow Transplant 2016;22(7):1152-1158.

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9. Savage SA, Dufour C. Classical inherited bone marrow failure syndromes with high risk for myelodysplastic syndrome and acute myelogenous leukemia. Semin Hematol 2017;54(2):105-114. 10. Alter BP, Giri N, Savage SA, Rosenberg PS. Cancer in the National Cancer Institute inherited bone marrow failure syndrome cohort after 15 years of follow-up. Haematologica 2017. 11. Alter BP, Caruso JP, Drachtman RA, Uchida T, Velagaleti GVN, Elghetany MT. Fanconi anemia: Myelodysplasia as a predictor of outcome. Cancer Genetics and Cytogenetics 2000;117(2):125-131. 12. Quentin S, Cuccuini W, Ceccaldi R, et al. Myelodysplasia and leukemia of Fanconi anemia are associated with a specific pattern of genomic abnormalities that includes cryptic RUNX1/AML1 lesions. Blood 2011;117(15):e161-170. 13. Mehta PA, Harris RE, Davies SM, et al. Numerical chromosomal changes and risk of development of myelodysplastic syndrome–acute myeloid leukemia in patients with Fanconi anemia. Cancer Genetics and Cytogenetics 2010;203(2):180-186. 14. Tonnies H, Huber S, Kuhl JS, Gerlach A, Ebell W, Neitzel H. Clonal chromosomal aberrations in bone marrow cells of Fanconi anemia patients: gains of the chromosomal segment 3q26q29 as an adverse risk factor. Blood 2003;101(10):3872-3874. 15. Peffault de Latour R, Soulier J. How I treat MDS and AML in Fanconi anemia. Blood 2016;127(24):2971-2979. 16. Ayas M, Saber W, Davies SM, et al. Allogeneic hematopoietic cell transplantation for fanconi anemia in patients with pretransplantation cytogenetic abnormalities, myelodysplastic syndrome, or acute leukemia. J Clin Oncol 2013;31(13):1669-1676. 17. Gondek LP, Tiu R, O'Keefe CL, Sekeres MA, Theil KS, Maciejewski JP. Chromosomal lesions and uniparental disomy detected by SNP arrays in MDS, MDS/MPD, and MDS-derived AML. Blood 2008;111(3):1534-1542. 18. da Silva FB, Machado-Neto JA, Bertini V, et al. Single-nucleotide polymorphism array (SNP-A) improves the identification of chromosomal abnormalities by metaphase cytogenetics in myelodysplastic syndrome. J Clin Pathol 2017;70(5):435-442. 19. Maciejewski JP, Tiu RV, O'Keefe C. Application of array-based whole genome scanning technologies as a cytogenetic tool in haematological malignancies. Br J Haematol 2009;146(5):479-488. 20. Stevens-Kroef MJ, Olde Weghuis D, ElIdrissi-Zaynoun N, et al. Genomic array as compared to karyotyping in myelodysplastic syndromes in a prospective clinical trial. Genes, Chromosomes & Cancer 2017;56(7):524-534. 21. Laurie CC, Laurie CA, Rice K, et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nature Genetics 2012;44(6):642-650. 22. Jacobs KB, Yeager M, Zhou W, et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nature Genetics 2012;44(6):651-658.

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Table 1. Characteristics of Patients for Proposal 1711-128 that have inborn abnormalities of erythrocyte differentiation and/or function and have recipient samples available

Related Unrelated

Variable N (%) N (%) Number of Recipients 26 250 Number of centers 12 42 Patient-related Recipient age at transplant

0-9 16 (62) 140 (56) 10-19 10 (38) 79 (32) 20-29 0 20 ( 8) 30-39 0 8 ( 3) 40-49 0 2 ( 1) 60+ 0 1 (<1) Median (range) 9 (3-17) 8 (0-63)

Recipient race / ethnicity Caucasian, non-Hispanic 11 (44) 178 (74) African-American, non-Hispanic 2 ( 8) 12 ( 5) Asian, non-Hispanic 3 (12) 11 ( 5) Native American, non-Hispanic 0 3 ( 1) Hispanic, Caucasian 9 (36) 36 (15) Hispanic, African-American 0 2 ( 1) Unknown 1 (N/A) 8 (N/A)

Recipient sex Male 14 (54) 144 (58) Female 12 (46) 106 (42)

Karnofsky score 10-80 5 (19) 25 (10) 90-100 21 (81) 222 (89) Missing 0 3 (1)

Recipient sample type Paired samples 20 (77) 217 (87) Recipient orphan samples 6 (23) 33 (13)


SAA 3 (12) 20 ( 8) Inherited abnormalities erythrocyte diff fxn 23 (88) 230 (92)

Subdisease at transplant Schwachmann-Diamond Syndrome 1 ( 4) 13 ( 5) Dyskeratosis congenital 3 (12) 20 ( 8) Fanconi anemia 13 (50) 170 (68) Diamond-Blackfan anemia 9 (35) 47 (19)


Bone marrow 20 (77) 155 (62)

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Related Unrelated Variable N (%) N (%)

Peripheral blood 4 (15) 95 (38) BM+UCB 2 ( 8) 0

GvHD prophylaxis No GVHD prophylaxis (forms under review) 1 ( 4) 0 Ex vivo T-cell depletion 1 ( 4) 62 (25) CD34 selection 2 ( 8) 56 (22) Post-transplant cyclophosphamide + others 4 (15) 0 Tacrolimus + MMF ± others 3 (12) 13 ( 5) Tacrolimus + MTX ± others (except MMF) 2 ( 8) 24 (10) Tacrolimus + others (except MTX, MMF) 1 ( 4) 5 ( 2) Tacrolimus alone 0 7 ( 3) CSA + MMF ± others (except Tacrolimus) 6 (23) 39 (16) CSA + MTX ± others (except Tacrolimus, MMF) 2 ( 8) 24 (10) CSA + others (except Tacrolimus, MTX, MMF) 3 (12) 5 ( 2) CSA alone 0 6 ( 2) Other GVHD prophylaxis 1 ( 4) 5 ( 2) Missing 0 4 ( 2)

Conditioning regimen Myeloablative 5 (19) 77 (31) Reduced Intensity 2 ( 8) 100 (40) Nonmyeloablative 4 (15) 22 ( 9) Other 15 (58) 51 (20)

Donor / recipient sex matching Donor male / recipient male 9 (35) 100 (40) Donor male/ recipient female 6 (23) 64 (26) Donor female / recipient male 5 (19) 44 (18) Donor female / recipient female 6 (23) 42 (17)

Donor / recipient CMV serostatus Negative / negative 6 (23) 65 (26) Negative / positive 1 ( 4) 33 (13) Positive / negative 2 ( 8) 39 (16) Positive / positive 2 ( 8) 59 (24) Unknown 15 (58) 54 (22)

Donor age at donation To be determined / missing 19 (73) 0 0-9 4 (15) 0 10-19 2 ( 8) 13 ( 5) 20-29 0 111 (44) 30-39 1 ( 4) 68 (27) 40-49 0 44 (18) 50+ 0 14 ( 6) Median (range) 10 (4-35) 30 (18-57)

Donor race / ethnicity Caucasian, non-Hispanic 5 (71) 26 (70)

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Related Unrelated Variable N (%) N (%)

African-American, non-Hispanic 2 (29) 2 ( 5) Asian, non-Hispanic 0 3 ( 8) Hispanic, Caucasian 0 2 ( 5) Hispanic, race unknown 0 4 (11) Unknown 19 (N/A) 213 (N/A)

Year of transplant 2005 0 14 ( 6) 2006 0 13 ( 5) 2007 0 15 ( 6) 2008 0 19 ( 8) 2009 0 19 ( 8) 2010 0 19 ( 8) 2011 1 ( 4) 16 ( 6) 2012 3 (12) 28 (11) 2013 0 26 (10) 2014 4 (15) 24 (10) 2015 5 (19) 21 ( 8) 2016 9 (35) 21 ( 8) 2017 4 (15) 15 ( 6)


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Proposal 1711-169

Title: The Effect of HLA Class I Heterozygosity and HLA Supertypes on Outcomes Following Allogeneic Hematopoietic Cell Transplant For Myeloid and Lymphoid Malignancies

Christine Camacho-Bydume: Memorial Sloan Kettering Cancer Center Katharine Hsu: Memorial Sloan Kettering Cancer Center

Hypothesis: Recent findings have demonstrated that heterozygosity of human leukocyte antigen (HLA) class I genotypes as well as the presence of specific HLA supertypes are associated with improved survival in patients treated with immune checkpoint therapy due to superior presentation of tumor antigen to antigen-specific T-cells. Because donor T cells are an important contributor to a graft-versus-leukemia effect in allogeneic hematopoietic cell transplantation (HCT), we hypothesize that HLA class I genotypes influence outcomes following 8/8 HLA-matched HCT for patients with acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), acute lymphoblastic leukemia (ALL), and lymphoma.

Specific aims: The primary aim of this study is to determine if heterozygosity at HLA class I alleles in donor- recipient pairs undergoing T-cell replete allogeneic 8/8-matched unrelated HCT for AML, MDS, ALL, and lymphoma is associated with outcomes of survival, relapse, graft-versus-host disease (GVHD), transplant-related mortality (TRM), and CMV reactivation.

The secondary aims of this study are: • To analyze the association of specific HLA class I supertypes in donor-recipient pairs with

outcomes of survival, relapse, GVHD, and TRM following HCT for AML, MDS, ALL and lymphoma.• To evaluate the association of HLA class I genotypes and supertypes in donor-recipient pairs in

T-cell replete versus T-cell deplete HCT for AML, MDS, ALL, and lymphoma with outcomes ofsurvival, relapse, GVHD, and TRM.

• To determine whether specific HLA class I supertypes of donor-recipient pairs are associatedwith cytomegalovirus (CMV) reactivation following HCT.

Scientific impact: Numerous studies have previously investigated the association of individual HLA class I alleles with various diseases, particularly autoimmune and inflammatory disorders. Recent advances in the field of checkpoint inhibitors have identified the impact of HLA class I genotype, specifically class I locus heterogeneity and class I supertypes, as a potential predictors of response to treatment and overall survival in solid tumor patients.1 In HCT, where the number of unrelated donor transplants has steadily increased, the majority of histocompatibility research has investigated HLA locus matching on transplant outcomes in efforts to determine permissible settings for mismatched allografts.2 The effect of heterozygosity of HLA class I loci on post-HCT outcomes, however, has not specifically been investigated. Identification of an association of increased heterozygosity of HLA class I and specific HLA supertypes with survival and transplant outcomes may reveal hierarchies of HLA-cell restriction as the basis of graft-versus-leukemia effect in HCT, stratify patients into prognostic categories, and potentially advance the donor selection process to minimize relapse and transplant-related mortality and increase survival.

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Scientific justification: Much of the extensive polymorphism characterizing HLA encodes the amino acid residues centered in the peptide-binding region of the HLA molecule, which is responsible for alerting antigen-specific members of T cell repertoire.3 Heterozygosity of HLA class I alleles in individuals would therefore potentially lead to greater diversity of peptide presentation to T-cells and subsequent activation of a diversified immune response. Indeed, a recent study demonstrated that heterozygosity at all HLA class I loci is associated with improved survival in patients with melanoma, non-small cell lung cancer, and other advanced cancers treated with immune checkpoint inhibitors when compared to patients homozygous in at least one HLA class I locus.1 The association of improved survival and heterozygosity of HLA class I remained significant in multivariable analysis, when tumor mutation burden, age, disease stage, and type of immune checkpoint inhibitor were taken into account.1 Researchers next classified HLA class I alleles into supertypes, which represent twelve discrete groupings of individual HLA alleles based on shared peptide binding specificity of the main anchor motif.3 By defining the chemical specificities of the B and F binding pockets of the HLA molecule as aliphatic, aromatic, basic, acidic, hydrophobic, etc, individual HLA alleles can be assigned to these supertypes and collectively analyzed based on these shared binding properties.3 In the aforementioned study, the “HLA B44 supertype”, which describes a total of 108 alleles, including B*18:01, B*44:02, B*44:03, B*44:05, and B*50:01, was found to be associated with improved survival in patients with melanoma treated with immune checkpoint inhibitors, while the “HLA B62 supertype”, which included 71 alleles, was associated with reduced survival.1,3

We hypothesize that these same concepts can be applied to HCT, where there is a similar intricate interaction of HLA class I genotypes, T-cells, and tumor cells in a microenvironment of inflammatory cytokines post-transplant. Previous case-control studies have shown an association of HLA class I alleles with leukemia: higher frequencies of HLA-B*45 and HLA-B*56 in Brazilian patients with ALL and HLA-B*7 in AML when compared to healthy controls;4 a higher frequency of HLA-B*40 among Mexican patients with ALL; 5 and HLA-B*44 in Moroccan patients with leukemia. 6 In addition, results from a recent genome-wide association study (GWAS) showed an association of diffuse large B-cell lymphoma with HLA-B*08. 7

Many of these case-control studies appear to show a predominance of HLA alleles belonging to the HLA-B44 supertype in patients with leukemia. It is possible that the immune response elicited by the B44 supertype against solid tumors in the setting of immune checkpoint inhibitors is similar to the response seen post-HCT, thus providing an immunogenic basis for improved disease control via graft-versus-leukemic effect, leading to higher overall survival. We hypothesize that the setting of allogeneic T-replete HCT may provide additional triggers, such as different cytokines and increased alloreactivity of T-cells, which may contribute to improved outcomes in cases of myeloid and lymphoid malignancies.

Study population: The study population will include patients >18 years of age, diagnosed with AML, MDS, ALL, and lymphoma who received an HLA-matched allogeneic HCT from an unrelated donor.

Data requirements: The proposed study does not require collection of any supplemental data outside of the current data collection forms as follows: Recipient Baseline Data (2000), Infectious Disease Markers (2004), Confirmation HLA Typing (2005), Acute Myelogenous Leukemia Pre-HCT Data (2010), MDS/MPD Pre-HCT Data (2014), Acute Myelogenous Leukemia Post-HCT Data (2110), MDS/MPD Post-HCT Data (2114), Pre-

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Transplant Essential Data (2402), and Post-Transplant Essential Data (2450).


• Age: 18-29 vs. 30-39 vs. 40-49 vs. 50-59 vs. ≥ 60 • Gender: male vs. female • Karnofsky score: <90 vs. 90-100%

Disease-related:

• Diagnosis: AML vs. MDS vs. ALL vs. lymphoma (NHL, HL) • Disease status at transplant: early vs. advanced

Transplant-related:

• Donor and recipient HLA typing and degree match • Year of transplant: 2000-2011 • Condition regimen intensity: myeloablative vs. non-myeloablative • Donor-recipient gender match: M/M vs. M/F vs. F/M vs. F/F • Source of stem cells: bone marrow vs. peripheral blood • Donor type: 10/10 HLA-A, -B, -C, -DR, and –DQ allele matched unrelated • Type of graft: T-cell depleted vs. T-cell replete • Post-transplant survival status: alive vs. dead

Sample requirements: This study does not require the use of biological specimens. Study design: In the proposed study, we will initially examine the heterozygosity of HLA class I alleles for donor- recipient pairs and its association with survival, relapse, GVHD, and TRM for allogeneic T-replete HCT. We will divide the donor-recipient pairs into disease-specific cohorts for AML, MDS, ALL, and lymphoma (NHL, HD). The initial analysis will examine outcomes of patients with heterozygosity at all class I loci compared with patients with homozygosity at one or more locus. If an effect is seen, we will then further examine if homozygosity at one particular locus is associated with clinical clinical outcome. We will also investigate whether the presence of the twelve specific HLA supertypes in donor-recipient pairs is associated with survival, relapse, GVHD, and TRM for allogeneic T-replete HCT. We hypothesize that the HLA-B44 supertype will likely play a role given that some of its member alleles have been found to be associated with myeloid and lymphoid malignancies in previous case-control studies. We will further compare the association of HLA class I heterozygosity and supertypes with outcomes of survival, relapse, GVHD, and TRM for T-replete versus T-depleted HCT in each disease cohort. We will then examine the association of HLA supertypes and CMV reactivation for each disease-specific cohort and compare these findings between T-replete and T-depleted HCT. Data source: This study will use the CIBMTR Research Database.

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References: 1. Chowell D, Morris LGT, Grigg CM, et al. Patient HLA class I genotype influences cancer response

to checkpoint blockade immunotherapy. Science 7 December 2017 (10.1126/science.aao4572). 2. Lazaryan A, Wang T, Spellman SR, et al. Human leukocyte antigen supertype matching after

myeloablative hematopoietic cell transplantation with 7/8 matched unrelated donor allografts: a report from the Center for International Blood and Marrow Transplant Research. Haematologica 2016;101 (10):1267-1274.

3. Sidney J, Peters B, Frahm N, et al. HLA class I supertypes: a revised and updated classification. BMC Immunol 2008;9:1-15.

4. Barion LA, Tsuneto LT, Testa GV, et al. Association between HLA and leukemia in a mixed Brazilian population. Rev Assoc Med Bras 2007;53(3):252-256.

5. Fernandez-Torres J, Flores-Jimenez D, Arroyo-Perez A, et al. HLA-B*40 allele plays a role in the development of acute leukemia in Mexican population: a case-control study. Biomed Res Int 13 November 2013 (10.1155/2013/705862).

6. Kabbaj M, Oudghiri M, Naya A, et al. HLA-A, -B, -DRB1 alleles and haplotype frequencies in Moroccan patients with leukemia. Ann Biol Clin (Paris) 2010;68(3):291-296.

7. McAulay KA and Jarrett RF. Human leukocyte antigens and genetic susceptibility to lymphoma. Tissue Antigens 2015;86(2):98-113.

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Table 1. Characteristics of Patients for Kathy Hsu proposal using high-resolution typed HLA cases that are 8/8 allele matched

Variable N (%) Number of Recipients 8402 Number of centers 175 Data source

CRF 5127 (61) TED 3275 (39)

Patient-related Recipient age at transplant

18-19 162 ( 2) 20-29 1105 (13) 30-39 1111 (13) 40-49 1608 (19) 50-59 2163 (26) 60+ 2253 (27) Median (range) 51 (18-84)

Recipient race Caucasian 7561 (92) African American 155 ( 2) Asian / Pacific Islander 159 ( 2) Hispanic 349 ( 4) Native American 26 (<1) Other 3 (<1) Unknown 149 (N/A)



AML 5342 (64) ALL 1637 (19) NHL 1226 (15) Hodgkin’s Lymphoma 197 ( 2)

Disease stage at transplant Early 3856 (46) Intermediate 1439 (17) Advanced 1369 (16) Other 1738 (21)

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Variable N (%) Transplant-related Stem cell source

Bone marrow 1737 (21) Peripheral blood 6665 (79)

GvHD Prophylaxis Tacrolimus + MMF ± others 1308 (16) Tacrolimus + MTX ± others (except MMF) 4805 (57) Tacrolimus + others (except MTX, MMF) 498 ( 6) Tacrolimus alone 261 ( 3) CSA + MMF ± others (except Tacrolimus) 622 ( 7) CSA + MTX ± others (except Tacrolimus, MMF) 792 ( 9) CSA + others (except Tacrolimus, MTX, MMF) 62 ( 1) CSA alone 54 ( 1)

Conditioning regimen Myeloablative 4893 (58) RIC / NMA 2020 (24) Other / to be determined 1489 (18)

Donor age at donation 18-19 273 ( 3) 20-29 3991 (48) 30-39 2312 (28) 40-49 1395 (17) 50+ 431 ( 5) Median (range) 30 (18-61)

Donor race Caucasian 6773 (90) African American 136 ( 2) Asian / Pacific Islander 142 ( 2) Hispanic 133 ( 2) Native American 77 ( 1) Multiple race 300 ( 4) Other 5 (<1) Unknown 836 (N/A)

Year of transplant 2000 165 ( 2) 2001 189 ( 2) 2002 193 ( 2) 2003 250 ( 3) 2004 337 ( 4)

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Variable N (%) 2005 452 ( 5) 2006 513 ( 6) 2007 608 ( 7) 2008 544 ( 6) 2009 578 ( 7) 2010 566 ( 7) 2011 586 ( 7) 2012 665 ( 8) 2013 851 (10) 2014 938 (11) 2015 870 (10) 2016 97 ( 1)


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TO: Immunobiology Working Committee Members

FROM: Stephanie Lee, MD, MPH; Co-Scientific Director for the Immunobiology WC Stephen Spellman, MBS; Co-Scientific Director for the Immunobiology WC

RE: Studies in Progress and Publication Summary

Studies in Progress Summary

HLA GENES IB12-02c: Prospective assignment of HLA-DPB1 T cell epitope (TCE) group mismatches by functional distance scores compared to the functional TCE assignment algorithm (K Fleischhauer) The primary objective of the study is to test whether the functional distance (dFD) scores adds to or replaces the TCE group assignment score for permissive and non-permissive HLA-DPB1 mismatches. The study showed similar predictive ability between the two scores but the dFD algorithm is preferred since all HLA-DPB1 alleles may be assigned using the in silico methods. A manuscript has been circulated.

IB13-09: The development of machine learning based classifiers to define the alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation (Y Louzoun) The main goal of this study is to classify alloreactivity of HLA Class I mismatches based on peptide repetoire differences and apply Machine Learning based classifiers to evaluate the impact on HCT outcome. This study did not show that machine learning-based classifiers added significant prognostic information to standard HLA information. Manuscript preparation is in progress.

IB14-08: Development and validation of a clinical unrelated donor selection score (B Shaw/SJ Lee) The aim of the study is to develop and validate a donor risk score for matched unrelated donors that helps to identify the optimal donor among those available. Results showed that only donor age was reproducibly associated with worse survival. The manuscript is under review.

IB15-01: The impact of single nucleotide gene polymorphisms (SNP) in the gamma block of the major histocompatibility complex (MHC) on unrelated donor hematopoietic cell transplants (HCT) for hematological malignancies (M Askar/R Sobecks) The objectives of this study are to investigate the incidence of gamma block SNPs in matched and mismatched URD transplantation, and to correlate these SNPs with clinical outcome. The gamma block contains inflammatory and regulatory genes and mismatching is an indicator of haplotype recombination. Results showed lack of predictive associations between gamma block matching or number of mismatches with major transplant outcomes. Manuscript preparation is in progress.

IB16-01: The role of HLA-E compatibility in the prognosis of acute leukemia patients undergoing 10/10 HLA matched unrelated HSCT (C Tsamadou/D Furst/J Mytilineos) The primary objective of the study is

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to investigate whether HLA-E donor-recipient incompatibility is associated with better HCT outcomes in a cohort of acute leukemia patients transplanted with HLA-A, B, C, DRB1 and DQB1 matched unrelated donors. Typing is in progress. IB16-02: Use of HLA structure and function parameters to understand the relationship between HLA disparity and transplant outcomes (LA Baxter-Lowe) The main objective of the study is to determine the relationship between HLA disparities ranked by their impact on T cell receptor docking, peptide binding and the combination of docking and binding. The docking and binding assignment algorithm has been locked and the analysis is in progress. CYTOKINE/CHEMOKINE IB14-03a: The prognostic impact of somatic mutations and levels of CXC chemokine ligands on post hematopoietic cell transplantation (HCT) outcomes in patients with myelodysplastic syndromes (MDS) (W Saber/B Dhakal) The primary objective of the study is to evaluate the plasma CXCL4 and CXCL7 chemokine levels in patients undergoing transplantation for MDS in comparison to those with other conditions. Although levels of CXCL4 and CXCL7 were lower in MDS compared to normal donors, results did not show an association of CXC chemokine-related variables and transplant outcomes. Manuscript preparation is in progress. NK/KIR R02-40/R03-63: Choosing donors with favorable KIR B genotypes for unrelated hematopoietic cell transplantation (HCT) results in superior relapse protection and better relapse-free survival for patients with acute myeloid leukemia (AML) (J Miller) This is an ongoing study in support of Dr. Miller’s NK Biology program project grant. Ongoing. IB15-02: Natural killer cell genomics and outcomes after HCT for chronic lymphocytic leukemia (V Bachanova/J Miller/D Weisdorf/S Cooley) The study is based on recent advances linking NK cell genetics with outcomes after allogeneic donor transplantation. The primary hypothesis is that the gene content regulating donor-derived NK cell function influences the graft-versus-leukemia reaction following allogeneic donor hematopoietic cell transplantation (HCT) for chronic lymphocytic leukemia (CLL). Results did not show significant associations between KIR variables and outcomes after transplant for CLL. Manuscript preparation is in progress. IB15-03: Effect of Killer immunoglobulin like receptors on allogeneic HCT for pediatric acute leukemia (M Verneris/J Miller/S Cooley) The primary aim of the study is to examine the association of donor KIR genotype (A/A vs B/x) on relapse and disease free survival (DFS) in children undergoing allogeneic transplantation (allo-HCT) with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Data file preparation is underway. IB17-02 Donor-recipient NK cell determinants associated with survival in JMML after hematopoietic stem cell transplantation (D Lee/H Rangarajan) The protocol is being finalized. OTHER GENES IB06-05: Use of high-resolution HLA data from the NMDP for the International Histocompatibility Working Group in HCT (E Petersdorf) This study proposes to identify novel major histocompatibility

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complex resident SNPs of clinical importance. HLA matched pairs were genotyped for 1120 MHC SNPs and correlated with outcomes. This is a collaborative study with the International Histocompatibility Working Group – HCT component (IHWG). Ongoing. IB09-04: Donor/recipient gene polymorphisms of drug metabolism and in innate immune response post allele-matched unrelated donor hematopoietic stem cell transplantation (HCT) (V Rocha) This study is designed to validate associations between polymorphisms in drug metabolism and innate immune response genes and outcomes previously identified in matched sibling donor HCT in the HLA-matched unrelated donor HCT setting. Manuscript preparation is underway. IB09-05: Identification of functional single nucleotide polymorphisms (SNPs) in umbilical cord blood transplant (E Petersdorf) The primary hypothesis of the study is that umbilical cord blood units and recipients differ for genome-wide single nucleotide polymorphism and gene copy number variation and that these differences may define putative transplant outcome determinants. This is a collaborative study with IHWG. Ongoing. IB09-06/RT09-04: Genetic susceptibility to transplant-related mortality after matched unrelated stem cell transplant (T Hahn) This is a joint study with the Regimen Related Toxicity working committee and is supported by an R01 grant to Drs. Hahn and Sucheston-Campbell. This study will test for a genetic association with transplant-related and overall mortality in recipients of myeloablative and reduced intensity conditioning matched unrelated donor HCT. Multiple oral abstracts were presented at the 2015, 2016 ASH and 2016, 2017 BMT Tandem meetings. Multiple manuscripts are in process. Ongoing. IB09-07: Clinical significance of genome-wide variation in unrelated donor hematopoietic stem cell transplantation (HCT) (E Petersdorf) This study is designed to assess the impact of genome-wide variation between donors and recepients in HLA matched unrelated donor HCT. This is a collaborative study with the IHWG. Ongoing. IB10-01d Flow Cytometry using FISH techniques in a Severe Aplastic Anemia population (S Gadalla/S Savage) This study was designed to use Flow-FISH techniques to measure telomere length in cellular subsets and correlate these measures with outcomes. Results show that B cell short telomere length is associated with deaths due to infection but not GVHD or graft failure. The paper is under review. IB14-04: Assessing the similarity of the T cell receptor repertoire in allogeneic hematopoietic stem cell recipients with the same single human leukocyte mismatches (EH Meyer) The goal of this study is to measure T cell alloreactivity following hematopoietic stem cell transplantation by examining cases where transplant recipients have the same HLA single, double or multiple HLA mismatch, to see if they develop similar alloreactivity. Control transplant recipients with the same HLA type who did not receive an HLA-mismatched transplant will be also analyzed. The number of samples was very limited and results were not conclusive. Manuscript preparation is in progress. IB14-05: mtDNA haplotypes and unrelated donor transplant outcomes (M Verneris/L Spector) The goal of this study is to test whether patient or donor mitochondrial haplotypes predict outcomes of unrelated donor transplantation, particularly GVHD, relapse and survival. The study is supported by an R01 grant to Drs. Verneris and Spector. Analysis is in progress. IB15-04: Clinical outcomes among hematopoietic stem cell transplant recipients as a function of socioeconomic status and related transcriptome differences (J Knight/JD Rizzo/S Cole) The primary

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hypothesis of the study is that increased expression of the conserved transcriptional response to adversity (CTRA) gene profile will be associated with lower socioeconomic status (SES) and worse clinical outcomes among a group of unrelated donor (URD) myeloablative (MA) acute myelogenous leukemia (AML) recipients in CR1. Results showed that very high or very low CTRA inflammatory gene profiles were associated with relapse and disease-free survival. The manuscript is being circulated.

IB15-05: Secondary findings in exome sequencing data (S Savage/S Gadalla) The goal of the study is to incorporate exome sequencing results from the IB10-01 cohort into a larger cohort of exome sequenced cases and controls from the NCI Division of Cancer Epidemiology and Genetics to evaluate the frequency of incidental findings. Analysis is ongoing. IB15-06b: Validation of Telomatch for donor telomere length and outcomes after hematopoietic stem cell transplantation for acute leukemia (S Gadalla/S Savage/DJ Loftus/E Hytopoulos/M Yeager/C Dagnall) The main objective of the study was to validate the utility of the TeloMatch prognostic scoring system, preliminary results presented as an oral abstract at the 2015 ASH annual meeting, on an independent cohort. Results did not confirm the predictive ability of the Telomatch score. The manuscript is under review. IB15-07: Functional genetic variants of the ST2 gene in pairs of recipient and donors for risk stratification of GVHD and TRM outcomes (S Paczesny) The serum biomarker sST2 is associated with an increased risk for therapy-resistant GVHD and death. The primary hypothesis is that similar to the heritability of sST2 in the Framingham Offspring Cohort explaining sST2 as a predictor of cardiovascular risk, the 16 SNPs most associated with sST2 will determine which donor /recipient pair is at risk of developing acute graft-versus-host disease (aGVHD) and transplant-related mortality (TRM) following allogeneic hematopoietic cell transplantation in a well-controlled cohort. The study will use the GWAS typing results and dataset from the DISCOVERY-BMT (IB09-06/RT09-04) cohort to evaluate the association of 16 sST2 SNPs. Results showed that ST2 SNPs correlated with sST2 levels but ST2 SNPs did not strongly correlate with GVHD. Manuscript preparation is underway IB16-03: Role of recipient and donor genetic polymorphisms in interferon lambda 4 (INFL4) on outcomes after unrelated allogeneic cell transplant (S Gadalla/L Prokunina-Olsson) The primary goal of the study is to evaluate the effect of recipient and donor genetic polymorphisms in the type-III interferon, interferon lambda 4 (INFL4) on outcomes following unrelated donor HCT for SAA and acute leukemia. The dG homozygous and heterozygous patients had higher TRM than the homozygous TT patients. Grade II-IV and III-IV acute GVHD were not different. Manuscript preparation is underway. IB17-03 Identification of genomic markers of post hematopoietic cell transplantation (HCT) outcomes in patients with myelofibrosis: A pilot study (W Saber/ S Gadalla) The goal of this study is to describe mutations associated with MF, and to correlate these abnormalities with clinical outcomes. Samples are being analyzed. IB17-04 Epigenetic profiling of unrelated donor-recipient pairs to improve donor selection during HCT transplants (S Beck/K Peggs/V Rakyan/A Webster) The goal of this study is to determine whether donor specific epigenetic patterns associate with risk of acute GVHD III-IV. and if so develop an epigenetic profile based donor selection algorithm. Sample analysis is underway.

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SENSITIZATION and TOLERANCE IB11-01a: Analysis of the NIMA effect on the outcome of unrelated PBSC/BM transplantation (G Ehninger/J van Rood/A Schmidt) The goal of this study is to determine whether matching for NIMA in the selection of unrelated donors can lead to better outcomes This is a collaborative study with the EBMT Immunobiology Working Party. A manuscript has been circulated. IB11-01b: Analysis of Inherited Paternal Antigen (IPA) effect on the outcome of unrelated PBSC/BM transplantation (G Ehniger/J Pingel/A Schmidt) The goal of this study is to determine whether donor and recipients with shared IPA experience lower relapse rates as previously observed in umbilical cord blood transplants. This is a collaborative study with the EBMT Immunobiology Working Party. The DKMS is collecting family HLA typing data to allow for the identification of IPA. IB14-06: Donor-Specific anti HLA antibodies, allele and antigen level HLA mismatches in the outcomes of transplantation of non-malignant diseases with unrelated donors (M Fernandez-Vina/A Woolfrey) The goal of this study is to test whether pre-existing donor-specific anti-HLA antibodies are associated with higher rates of graft failure and worse outcomes in HLA-mismatched unrelated donor transplants for non-malignant diseases. Results did not confirm that DSAs predicted graft failure but the power of the analysis was limited. A manuscript is under review.

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Publication Summary – Published manuscripts IB08-08 Goyal RK, Lee SJ, Wang T, Trucco M, Haagenson M, Spellman SR, Veneris M, Ferrell RE. Novel HLA-DP region susceptibility loci associated with severe acute GvHD. Bone Marrow Transplant. 2017 Jan 1;52(1):95-100. doi:10.1038/bmt.2016.210. Epub 2016 Sep 5.

This study was a case-control design (grade III-IV versus no acute GvHD) and pooled GWA approach to study European-American recipients with hematological malignancies who received myeloablative conditioning non-T-cell-depleted first transplantation from HLA-A, -B, -C, -DRB1, -DQB1 allele level (10/10) matched unrelated donors. DNA samples were tested using the Affymetrix Genome-wide SNP Array 6.0. We identified three novel susceptibility loci in the HLA-DP region of recipient genomes that were associated with III-IV acute GvHD (rs9277378, P=1.58E-09; rs9277542, P=1.548E-06 and rs9277341, P=7.718E-05). Of these three single nucleotide polymorphisms (SNPs), rs9277378 and rs9277542 are located in non-coding regions of the HLA-DPB1 gene and the two are in strong linkage disequilibrium with two other published SNPs associated with acute GvHD, rs2281389 and rs9277535. Eighteen other recipient SNPs and 3 donor SNPs with a high level of significance (8E-07 or lower) were found. Our report contributes to emerging data showing clinical significance of the HLA-DP region genetic markers beyond structural matching of DPB1 alleles.

IB12-04 Hoff GA, Fischer JC, Hsu K, Cooley S, Miller JS, Wang T, Haagenson M, Spellman S, Lee SJ, Uhrberg M, Venstrom JM, Verneris MR. Recipient HLA-C haplotypes and miRNA 148a/b binding sites have no impact on allogeneic hematopoietic cell transplantation outcomes. Biol Blood Marrow Transplant. 2017 Jan 1;23(1):153-160. doi:10.1016/ j.bbmt.2016.09.028. Epub 2016 Oct 13.

Previous single-center studies showed that recipients homozygous for the C1 epitope (C1/C1) experienced a survival benefit. Additionally, mismatching at HLA-C was beneficial in recipients possessing at least 1 C2 allele, whereas the opposite was true for homozygous C1 (C1/C1) recipients where HLA-C mismatching resulted in worse outcomes. In this large registry cohort, we were unable to confirm the prior findings regarding recipient HLA-C epitope status and outcome. Additionally, HLA-C surface expression (ie, surface density), as predicted by the miR-148a/b binding single nucleotide polymorphism, was also not with associated transplant outcomes.

IB13-05 Askar M, Sobecks R, Wang T, Haagenson M, Majhail N, Madbouly A, Thomas D, Zhang A, Fleischhauer K, Hsu K, Verneris M, Lee SJ, Spellman SR, Fernández-Viña M. MHC class I chain-related gene A (MICA) donor-recipient mismatches and MICA-129 polymorphism in unrelated donor hematopoietic cell transplantations has no impact on outcomes in acute lymphoblastic leukemia, acute myeloid leukemia, or myelodysplastic syndrome: A Center for International Blood and Marrow Transplant Research study. Biol Blood Marrow Transplant. 2017 March 1;23(3):436-444. doi.org/10.1016/j.bbmt.2016.11.021. Epub 2016 Dec 14.

In this study, we investigated the association of MICA polymorphism (MICA-129, MM versus MV versus VV) and MICA mismatches after HCT with 10/10 HLA-matched (n = 552) or 9/10 (n = 161) unrelated donors. Our results showed that neither MICA mismatch nor MICA-129 polymorphism were associated with any transplantation outcome (P < .01), with the exception of a higher relapse in recipients of MICA-mismatched HLA 10/10 donors (hazard ratio [HR], 1.7; P = .003). There was a suggestion of association between MICA mismatches and a higher risk of acute GVHD grades II to IV (HR, 1.4; P = .013) There were no significant interactions between MICA mismatches and HLA matching (9/10 versus 10/10). In conclusion, the findings in this

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cohort did not confirm prior studies reporting that MICA polymorphism and MICA mismatches were associated with HCT outcomes.

IB14-03b Lindsley RC, Saber W, Mar B, Medd, Wang T, Haagenson M, Grauman P, Zhu Z, Spellman S, Lee SJ, Verneris M, Hsu K, Fleischhauer K, Cutler C, Antin JH, Neuberg D, Ebert BL. Prognostic Mutations in Myelodysplastic Syndrome After Stem Cell Transplantation. New Engl J Med. 2017 Feb 9; 376(6):536-547. doi.org/10.1056/NEJMoa1611604. Epub 2017 Feb 9.

We performed targeted mutational analysis on samples obtained before transplantation from 1514 patients with MDS. TP53 mutations were present in 19% of the patients and were associated with shorter survival and a shorter time to relapse than was the absence of TP53 mutations, after adjustment for significant clinical variables (P<0.001 for both comparisons). Among patients 40 years of age or older who did not have TP53 mutations, the presence of RAS pathway mutations was associated with shorter survival than was the absence of RAS pathway mutations (P=0.004), owing to a high risk of relapse, and the presence of JAK2 mutations was associated with shorter survival than was the absence of JAK2 mutations (P=0.001), owing to a high risk of death without relapse. The adverse prognostic effect of TP53 mutations was similar in patients who received reduced-intensity conditioning regimens and those who received myeloablative conditioning regimens. By contrast, the adverse effect of RAS pathway mutations on the risk of relapse, as compared with the absence of RAS pathway mutations, was evident only with reduced-intensity conditioning (P<0.001). In young adults, 4% of the patients had compound heterozygous mutations in the Shwachman-Diamond syndrome-associated SBDS gene with concurrent TP53 mutations and a poor prognosis. Mutations in the p53 regulator PPM1D were more common among patients with therapy-related MDS than those with primary MDS (15% vs. 3%, P<0.001).

IB12-03 Madbouly A, Wang T, Haagenson M, Paunic V, Vierra-Green C, Fleischhauer K, Hsu KC, Verneris MR, Majhail NS, Lee SJ, Spellman SR and Maiers. Investigating the association of genetic admixture and donor/recipient genetic disparity with transplant outcomes. Biol Blood Marrow Transplant. 2017 June 1; 23(6):1029-1037. doi.org/10.1016/j.bbmt.2017.02.019. Epub 2017 March 2.

We studied the effect of 2 measures of genetic similarity in 1378 recipients who underwent myeloablative first allogeneic hematopoietic cell transplantation between 1995 and 2011 and their unrelated 10 of 10 HLA-A, -B, -C, -DRB1, and-DQB1- matched donors. The studied factors were as follows (1) donor and recipient genetic ancestral admixture and (2) pairwise donor/recipient genetic distance. Increased African genetic admixture for either transplant recipients or donors was associated with increased risk of overall mortality (hazard ratio [HR], 2.26; P = .005 and HR, 3.09; P = .0002, respectively) and transplant-related mortality (HR, 3.3; P = .0003 and HR, 3.86; P = .0001, respectively) and decreased disease-free survival (HR, 1.9; P = .02 and HR, 2.46; P = .002 respectively). The observed effect, albeit statistically significant, was relevant to a small subset of the studied population and was notably correlated with self-reported African-American race. We were not able to control for other nongenetic factors, such as access to health care or other socioeconomic factors; however, the results suggest the influence of a genetic driver. Our findings confirm what has been previously reported for African-American recipients and show similar results for donors. No significant association was found with donor/recipient genetic distance.

IB15-06c Wang Y, Wang T, Dagnall C, Haagenson M, Spellman SR, Hicks B, Jones K, Lee SJ, Savage SA, Gadalla SM. Relative telomere length before hematopoietic cell transplantation and outcome after

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unrelated donor hematopoietic cell transplantation for acute leukemia. Biol Blood Marrow Transplant. 2017 July 1;23(7):1054-1058. doi.org/10.1016/j.bbmt.2017.03.032. Epub 2017 Apr 4.

We studied 536 acute leukemia patients from those who underwent myeloablative 8/8 HLA-matched unrelated donor HCT between 2005 and 2012 and who had an available pre-HCT blood sample in the repository. Relative telomere length (RTL) was measured by real-time quantitative PCR. Pre-HCT RTL in acute leukemia patients was not statistically significantly associated with any outcome.

R04-74e Boudreau JE, Giglio F, Gooley TA, Stevenson PA, Le Luduec J-B, Shaffer BC, Rajalingam R, Hou L, Hurley CK, Noreen H, Reed EF, Yu N, Vierra-Green C, Haagenson M, Malkki M, Petersdorf EW, Spellman S, Hsu K. KIR3DL1/HLA-B subtypes govern acute myelogenous leukemia relapse after hematopoietic cell transplantation. J Clin Oncol. 2017 July 10;35(20):2268-2278. doi.org/10.1200/JCO.2016.70.7059. Epub 2017 May 18.

By using an algorithm that was based on polymorphism-driven expression levels and specificities, we predicted and tested inhibitory and cytotoxic NK potential on the basis of KIR3DL1/HLA-B subtype combinations in vitro and evaluated their impact in 1,328 patients with AML who underwent HCT from 9/10 or 10/10 HLA-matched unrelated donors. KIR3DL1 and HLA-B subtype combinations that were predictive of weak inhibition or noninhibition were associated with significantly lower relapse (hazard ratio [HR], 0.72; P = .004) and overall mortality (HR, 0.84; P = .030) compared with strong inhibition combinations. The greatest effects were evident in the high-risk group of patients with all KIR ligands (relapse: HR, 0.54; P < .001; and mortality: HR, 0.74; P < .008). Beneficial effects of weak and noninhibiting KIR3DL1 and HLA-B subtype combinations were separate from and additive to the benefit of donor activating KIR2DS1.

IB12-02B Fleischhauer K, Ahn KW, Wang H-L, Zito L, Crivello P, Müller C, Verneris M, Shaw BE, Pidala J, Oudshorn M, Lee SJ and Spellman SR. Directionality of non-permissive HLA-DPB1 T-cell epitope group mismatches in 8/8 matched unrelated donor hematopoietic cell transplantation. Bone Marrow Transplant. doi.org/10.1038/bmt.2017.96. Epub 2017 June 5.

We show here significantly higher in vitro relative responses (RR) to bi-directional mismatches compared with uni-directional HvG or GvH mismatches in a total of 420 one-way mixed lymphocyte reactions between 10/10 matched pairs (RR 27.5 vs 7.5 vs 15.5, respectively, P<0.001). However, in 3281 8/8 matched UD HCT for leukemia or myelodysplastic syndrome, the hazards of transplant-related mortality (TRM) were similar for uni-directional HvG or GvH mismatches and bi-directional mismatches (hazard ratio (HR) 1.32, P=0.001 vs HR 1.28, P=0.005 and HR 1.34, P=0.046), compared with permissive mismatches. Similar results were observed for overall survival. No statistical differences between the uni- and the bi-directional non-permissive groups were detected in pairwise comparisons for any of the outcomes tested. We conclude that consideration of directionality does not improve risk stratification by non-permissive HLA-DPB1 TCE mismatches in UD searches.

IB13-01 Eapen M, Wang T, Veys PA, Boelens JJ, St Martin A, Spellman S, Bonfim CS, Brady C, Cant AJ, Dalle J-H, Davies SM, Freeman J, Hsu KC, Fleischhauer K, Kenzey C, Kurtzberg J, Michel G, Orchard PJ, Paviglianiti A, Rocha V, Veneris MR, Volt F, Wynn R, Lee SJ, Horowitz MM, Gluckman E, Ruggeri A. Allele-level HLA matching for umbilical cord blood transplantation for non-malignant diseases in children: A retrospective analysis. Lancet Haematol 2017 July 1;4(7):e325-e333. doi.org/10.1016/S2352-3026(17)30104-7. Epub 2017 June 13.

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We retrospectively studied 1199 paediatric donor-recipient pairs with allele-level HLA matching who received a single unit umbilical cord blood transplantation for non-malignant diseases reported to the Center for International Blood and Marrow Transplant Research or Eurocord and European Group for Blood and Marrow Transplant. Compared with HLA-matched transplantations, mortality was higher with transplantations mismatched at two (hazard ratio [HR] 1·55, 95% CI 1·08-2·21, p=0·018), three (2·04, 1·44-2·89, p=0·0001), and four or more alleles (3·15, 2·16-4·58, p<0·0001). There were no significant differences in mortality between transplantations that were matched and mismatched at one allele (HR 1·18, 95% CI 0·80-1·72, p=0·39). Other factors associated with higher mortality included recipient cytomegalovirus seropositivity (HR 1·40, 95% CI 1·13-1·74, p=0·0020), reduced intensity compared with myeloablative conditioning regimens (HR 1·36, 1·10-1·68, p=0·0041), transplantation of units with total nucleated cell dose of more than 21 × 107 cells per kg compared with 21 × 107 cells per kg or less (HR 1·47, 1·11-1·95, p=0·0076), and transplantations done in 2000-05 compared with those done in 2006-12 (HR 1·64, 1·31-2·04, p<0·0001). Graft failure was the predominant cause of mortality. These data support a change from current practice in that selection of unrelated umbilical cord blood units for transplantation for non-malignant diseases should consider allele-level HLA matching at HLA-A, HLA-B, HLA-C, and HLA-DRB1.

IB09-06/RT09-04b Clay-Gilmour AI, Hahn T, Preus LM, Onel K, Skol A, Hungate E, Zhu Q, Haiman CA, Stram DO, Pooler L, Sheng X, Yan L, Liu Q, Hu Q, Liu S, Battaglia S, Zhu X, Block AW, Sait SNJ, Karaesmen E, Rizvi A, Weisdorf D, Ambrosone CB, Tritchler D, Ellinghaus E, Ellinghaus D, Stanulla M, Clavel J, Orsi L, Spellman SR, Pasquini MC, McCarthy PL, Sucheston-Campbell LE. Genetic association with B-cell acute lymphoblastic leukemia in allogeneic transplant patients differs by age and sex. Blood Advances. 2017 Sept 8;1(20):1717-1728. doi.org/10.1182/bloodadvances.2017006023. epub 2017 Sept 12.

The GWAS included 446 B-ALL cases, and 3027 healthy unrelated blood and marrow transplant (BMT) donors as controls from the Determining the Influence of Susceptibility Conveying Variants Related to One-Year Mortality after BMT (DISCOVeRY-BMT) study. We identified 1 novel variant, rs189434316, significantly associated with odds of normal cytogenetic B-ALL (odds ratio from meta-analysis [ORmeta] = 3.7; 95% confidence interval [CI], 2.5, 6.2; P value from meta-analysis [Pmeta] = 6.0 × 10-9). The previously reported pediatric B-ALL GWAS variant, rs11980379 (IKZF1), replicated in B-ALL pediatric patients (ORmeta = 2.3; 95% CI, 1.5, 3.7; Pmeta = 1.0 × 10-9), with evidence of heterogeneity (P = .02) between males and females. Sex differences in single-nucleotide polymorphism effect were seen in those >15 years (OR = 1.7; 95% CI, 1.4, 2.2, PMales = 6.38 × 10-6/OR = 1.1; 95% CI, 0.8, 1.5; PFemales = .6) but not ≤15 years (OR = 2.3; 95% CI, 1.4, 3.8; PMales = .0007/OR = 1.9; 95% CI, 1.2, 3.2; PFemales = .007). The latter association replicated in independent pediatric B-ALL cohorts. A previously identified adolescent and young-adult onset ALL-associated variant in GATA3 is associated with B-ALL risk in those >40 years. Our findings provide more evidence of the influence of genetics on B-ALL age of onset and we have shown the first evidence that IKZF1 associations with B-ALL may be sex and age specific.

IB14-01 William BM, Wang T, Haagenson M, Fleischhauer K, Verneris M, Hsu KC, de Lima MJ, Fernandez-Vina M, Spellman SR, Lee SJ, Hill BT. Impact of human leukocyte antigen (HLA) alleles on outcomes of allogeneic transplantation for B-cell non-Hodgkin lymphomas: A Center for International Blood and Marrow Transplant Research analysis. Biol Blood Marrow Transplant. doi.org/10.1016/j.bbmt.2017.11.003. Epub 2017 Nov 16.

We tested previously reported HLA-outcome associations in NHL by retrospectively

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evaluating whether the presence of HLA-A2, HLA-C1C1, HLA-DRB1*01:01, or HLA-DRB1*13 alleles or the presence of HLA-A1+, HLA-A2-, and HLA-B44- haplotypes is associated with outcomes in a cohort of 1314 HLA-8/8 matched sibling or unrelated donor HCT for relapsed/refractory B-NHL. We observed no significant association between any HLA allele or haplotype and overall survival or any of the secondary endpoints.

IB15-06a Gadalla SM, Wang T, Loftus D, Friedman L, Dagnall C, Haagenson M, Spellman SR, Buturovic L, Blauwkamp M, Shelton J, Fleischhauer K, Hsu KC, Verneris MR, Krstajic D, Hicks B, Jones K, Lee SJ, Savage SA. Donor telomere length and outcomes after allogeneic unrelated hematopoietic cell transplant in patients with acute leukemia. Bone Marrow Transplant. doi.org/10.1038/s41409-017-0029-9. Epub 2017 Dec 21.

We studied 1097 patients who received 8/8 HLA-matched unrelated HCT for acute myeloid leukemia (AML) or acute lymphocytic leukemia (ALL) between 2004 and 2012 with myeloablative conditioning. We used qPCR for relative telomere length (RTL) measurement, and Cox proportional hazard models for statistical analyses. In a discovery cohort of 300 patients, longer donor RTL (>25th percentile) was associated with reduced risks of relapse (HR = 0.62, p = 0.05) and acute graft-versus-host disease II-IV (HR = 0.68, p = 0.05), and possibly with a higher probability of neutrophil engraftment (HR = 1.3, p = 0.06). However, these results did not replicate in two validation cohorts of 297 and 488 recipients. In a combined analysis of the three cohorts, no statistically significant associations (all p > 0.1) were found between donor RTL and any outcomes.

Publication Summary – Submitted manuscripts – see above for description of results

IB10-01d Flow Cytometry using FISH techniques in a Severe Aplastic Anemia population. Gadalla S, Aubert G, Wang T, Haagenson M, Spellman SR, Wang L, Katki HA, Savage S, Lee SJ. Submitted. Blood.

IB13-08 Prediction of Acute Graft-Versus-Host Disease Following Hematopoietic Cell Transplantation (Lee C, Haneuse S, Wang H, Rose S, Spellman SR, Verneris M, Hsu K, Fleischhauer K, Lee SJ, Abdi R) Accepted. PLOS1.

IB14-06 Donor-directed HLA-specific antibodies in unrelated hematopoietic cell transplantation for non-malignant disorders. Woolfrey A, Wang T, Lee SJ, Haagenson MD, Chen G, Fleischhauer K, Horan J, Hsu K, Tyan D, Verneris M, Spellman SR, Fernandez-Vina M. Submitted. Blood.

IB14-08 Development and validation of a clinical unrelated donor selection score. Shaw BE, Logan BR, Spellman SR, Marsh SGE, Robinson J, Pidala J, Hurley C, Barker J, Maiers M, Dehn J, Wang H, Haagenson M, Porter D, Petersdorf EW, Woolfrey A, Horowitz MM, Verneris M, Hsu KC, Fleischhauer K, Lee SJ. Submitted. Biol Blood Marrow Transplant.

IB15-06b Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection. Buturovic L, Shelton J, Spellman SR, Wang T, Friedman L, Loftus D, Hesterberg L, Woodring T, Fleischhauer K, Hsu KC, Verneris MR, Haagenson M, Lee SJ. Accepted. Biol Blood Marrow Transplant.

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2017 TANDEM BMT MEETINGS (ASBMT/CIBMTR) ABSTRACT

Graft-versus-leukemia responses in chronic lymphocytic leukemia (CLL) are not influenced by natural killer cell (NK) KIR immunogenetics: a CIBMTR analysis

Veronika Bachanova, Daniel Weisdorf, Tao Wang, Stephen Spellman, Xiaochun Zhu, Stephanie J Lee, Katharina Fleischhauer, Katharine Hsu, Michael Verneris, Sarah Cooley, and Jeff Miller

Background: Allogeneic donor transplantation (alloHCT) can cure CLL partly by inducing a potent donor-derived graft-versus-leukemia (GVL) response. Donor NK cell receptor genetics has been associated with enhanced GVL responses and better survival in AML and lymphoma; however, the influence on post-transplant survival in CLL is unknown.

Methods: We identified 573 donor-recipient pairs from 106 centers transplanted between 1995-2014 using unrelated adult donor (UD) grafts. Donor DNA was typed for gene content of 15 individual killer immunoglobulin receptor (KIR) genes. Each donor was assigned a KIR haplotype (A or B) based on the presence of activating and inhibitory KIR genes. To test the donor-recipient NK cell genetic interactions, we examined 4 models: 1. KIR AA vs Bx haplotype (including Cen/Tel and KIR score 1-4), 2. Missing ligand (HLA-C1 vs -C2), 3. Missing Bw4 ligand for inhibitory KIR3DL1, 4. C1/C1 vs C2/2 vs C2/C1 in the presence of activating KIR2DS1. The main objective was to determine the strength of each model to predict relapse and survival.

Results: Median age was 56 years (range 21-74). Most patients were not in remission (65%). Most transplants were RIC (77%), 38% applied in vivo T cell depletion followed by 10/10 HLA-matched UD (66%) or 9/10 UD (34%) graft. Most recipients expressed KIR ligands HLA-C1 (82%) and also HLA-Bw4 (58%). Donor KIR polymorphism reflected the general population: 66% were Bx haplotype and 34% were AA. We found no interactions between KIR/KIR ligand and clinical variables. Multivariate analysis of the impact of donor KIR haplotype (AA vs Bx) and KIR content score showed similar incidence of non-relapse mortality, relapse, acute graft-versus-host disease (GVHD), and chronic GVHD and the same leukemia-free survival (LFS) or overall survival rate. All described models showed (prespecified P<0.01 to correct for multiple testing) lack of the prognostic value of NK receptor genomics on transplant outcomes. Factors significantly affecting OS included RIC regimen (HR of death 0.76; p=0.05), disease status (non-remission HR 1.96; p=0.003), donor type (9/10 UD HR 2.01; p=0.0004), Karnofsky score (>80% HR 0.46; p0.0006), and >3 lines of prior therapy (HR 2.54; p=<0.001).

Conclusion: Using a large cohort of patients reported to CIBMTR, we demonstrate that donor NK cell KIR/KIR-ligand genetic polymorphisms have no predictive value in CLL alloHCTs supporting the notion that NK cell interactions are specific to the lineage of the underlying leukemia.

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2017 AMERICAN SOCIETY OF HEMATOLOGY (ASH) MEETINGS ABSTRACT

Analysis of 10,462 8/8 HLA- matched unrelated donor transplants could not identify a donor selection score, as younger age is the only significant donor characteristic associated with survival

Bronwen E. Shaw, Brent R. Logan, Stephen R. Spellman, Steven GE Marsh, James Robinson, Joseph Pidala, Carolyn Hurley, Juliet Barker, Martin Maiers, Jason Dehn, Hailin Wang, Mike Haagenson, David Porter, Effie W. Petersdorf, Ann Woolfrey, Mary M. Horowitz, Michael Verneris, Katharine C. Hsu, Katharina Fleischhauer, and Stephanie J. Lee

Background: There is no hierarchical algorithm that weights the characteristics of individual donors against each other in a quantitative manner to facilitate donor selection when multiple potential equally HLA-matched unrelated donors (URD) are available. Donor factors, such as age, sex, CMV status, ABO type, and matching of secondary HLA loci (DQB1, DPB1), have been associated with recipient survival in URD hematopoietic cell transplantation (HCT) although the impact of specific factors has varied among studies. The goal of this study was to develop and validate a donor selection score that prioritizes donor characteristics associated with better survival in 8/8 HLA-matched URD transplantation.

Methods: Two large CIBMTR patient datasets were studied: HCT from 1999-2011 (n=5952) and 2012-2014 (n=4510). Patients were adults (>18), transplanted for acute myelogenous leukemia (AML), acute lymphocytic leukemia (ALL), chronic myelogenous leukemia (CML), or myelodysplastic syndrome (MDS). Each dataset was randomly split for the analysis. Cohort 1 (c1): 2/3 (n=3969) for modeling/score development (training) and 1/3 (n=1983) for testing and similarly for cohort 2 (c2): 2/3 (n=3051) and 1/3 (n=1459). Thus, two independent models were built and tested, adjusting for significant patient characteristics associated with survival. Interactions between donor characteristics, and donor and recipient characteristics were tested. The following donor characteristics were considered for the donor score: HLA-DQB1 matching, HLA-DPB1 matching (using the T-cell epitope matching categorization), age, sex matching, parity, CMV matching, ABO matching and race matching.

Results: In the final survival model (training set from 1999-2011, c1) we found significant negative associations with survival for three donor risk factors: non-permissive DPB1 matching (HR 1.13; 95% CI 1.01, 1.26; p-value=0.032), older donor age (as a linear effect, HR 1.07 per decade increase in age; 95% CI 1.02, 1.12, p-value=0.004), and CMV mismatching for CMV+ recipients (HR 1.14; 95% CI 1.02, 1.27; p-value=0.022). For CMV- recipients, a CMV+ donor was not significantly associated with an increase in mortality (HR=1.03; 95% CI 0.89-1.20; p-value=0.68), so this was not included in the score. ABO mismatching (any type: major, minor or bidirectional) was associated with mortality in initial modelling, but the effect was not present in more recent transplants (HR for ABO mismatch among patients transplanted since 2007: 1.04; 95% CI 0.91-1.19; p-value=0.638), so it was not included in the final model and donor score. Based on these results a donor risk score was constructed, however this score

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was not validated in the testing set (c1), nor were any of the individual component donor factors significantly associated with worse overall survival. In the second cohort (c2), only donor age was significantly associated with worse survival, and it validated in the independent test set from c2. Since donor age was significant in 3 of the 4 cohorts, we quantified the impact of donor age in the validation set of the most recent cohort, c2. We found that choosing a donor 2, 5, 10 or 20 years older was associated with a 1%, 2%, 3% or 7% decrease in 2 year OS, adjusted for patient characteristics. Conclusion: Despite data on over 10,000 URD transplants, we were unable to develop a valid donor selection score. The only donor characteristic associated with better survival was younger age, with 2-year survival being 3% better when a donor is 10 years younger. We did not test other endpoints; it is possible that separate scores could be generated to predict the risk of other outcomes (e.g. graft failure, graft-versus-host disease), however, unless the adverse donor characteristics are identical for these outcomes, centers will still have to prioritize the various donor characteristics to select from a pool of potential donors. This large data set shows that none of the other easily available donor clinical and genetic factors tested were reproducibly associated with survival and hence, flexibility in selecting URD based on these characteristics is justified. These data support a simplified URD selection process and have significant implications for URD registries.

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Analysis of Single Nucleotide Polymorphisms (SNP) Donor/Recipient Mismatches in the Gamma Block of the Major Histocompatibility Complex (MHC) And Their Association With Hematopoietic Cell Transplantation (HCT) Outcomes: A CIBMTR Study

Medhat Askar, David Sayer, Tao Wang, Michael Haagenson, Stephen R. Spellman, Stephanie J. Lee, Katharina Fleischhauer, Katharine Hsu, Michael Verneris, Dawn Thomas, Aiwen Zhang, Ronald Sobecks, and Navneet Majhail

Background: HLA haplotype mismatches have been associated with higher risk of acute graft versus host disease (aGVHD) in patients receiving HLA matched MUD HCT. However, beyond the HLA-A, B, C, DRB1, DQB1 and DPB1 loci currently considered for matching in clinical HCT, the MHC contains > 400 genes that may encode for unidentified transplant antigens. The Gamma block (GB) is located in the central MHC region between beta and delta blocks (encoding C & B and DR & DQ antigens, respectively). GB contains many inflammatory and immune regulatory genes such as Bf, C2 and C4 genes. A single center study showed that mismatches in SNP c.2918+98G, c.3316C, and c.4385C (ref. seq. C4A NG_011638.1) in the GB block (C4 SNP) were associatedwith higher risk of grades 3-4 aGVHD. We investigated whether GB mismatches are associatedwith worse HCT outcomes.

Methods: We investigated the association of GB SNP (GBS) mismatches with outcomes after 10/10 & 9/10 MUD HCT. Included were adult recipients after a first unrelated bone marrow or peripheral blood HCT for ALL, AML, or MDS reported to the Center for CIBMTR between 1999 and 2011. The primary outcome was aGVHD. OS, DFS, TRM, relapse, cGVHD, and engraftment were also analyzed. Variables considered in multivariate analyses included patient, disease and transplant characteristics. To adjust for multiple testing, a p value < 0.01 was considered significant. DNA samples were GBS genotyped by identifying 330 SNPs across 20kb using the Illumina NGS platform.

Results: The study included 700 donor/recipient pairs of which 77% were 10/10 HLA matched and the remaining were 9/10 mismatched at HLA-B to maximize the GBS mismatching. The overall 100 day incidence of aGVHD grades 2-4 & 3-4 were 41% and 17%, respectively. The overall incidence of matching at all GBS tested and at C4 SNP were 23% & 81%, respectively. Neither being matched across all GB SNP tested (vs. mismatched at 1+ SNPs) nor having higher number of GBS mismatches (treated as a continuous variable) was associated with transplant outcomes. There was no association between C4 SNP mismatches and outcomes except an unexpected significant association between having 2 C4 SNP mismatches and higher hazard ratio for relapse (HR: 3.38, 95% CI: 1.75-6.53, p=0.0003).

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Conclusion: These data did not support the hypothesis that mismatching at GB either globally (match vs. mismatch) or based on the cumulative number of SNP mismatches is associated with outcomes after HCT.

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Genome-Wide Significant Donor Genetic Associations with Death due to Disease in AML and MDS Patients in the First 1 Year after BMT Are Not Modified By Conditioning Intensity or TBI

Ezgi Karaesmen, Abbas Rizvi , Leah Preus, Philip L. McCarthy, Marcelo Pasquini, Sachidanand Singh, Sandeep Kumar Singh , Kenan Onel, Xiaochun Zhu, Stephen Spellman, Christopher A. Haiman, Daniel O. Stram, Loreall Pooler, Xin Sheng, Qianqian Zhu, Li Yan, Qian Liu, Qiang Hu, Song Liu, Alyssa I. Clay-Gilmour, Sebastiano Battaglia, David Tritchler, Theresa Hahn, Lara E Sucheston-Campbell

The DISCOVeRY-BMT genome-wide association study previously reported significant genetic variants associated with OS, PFS, TRM and death due to disease (DD) within 1 year after HLA-matched unrelated donor BMT. We performed a gene-drug interaction study in these patients to determine whether the effect of genetic variants in donors on DD differed by conditioning intensity (myeloablative (MA) or reduced (RIC)) as we previously identified multiple functional regions in donor genomes on chromosomes 5, 9 and 14 associated with DD. Since disease status pre-BMT is associated with DD, we also investigated the impact of early (in complete remission) vs advanced (not in complete remission) disease on our results.

From about 9 million typed and imputed SNPs available on donors in Cohorts 1 (N1=2,111) and 2 (N2=777), we selected the significant SNPs with main effects on DD using a threshold of P<1x105. A Cox-proportional hazard model including a term reflecting SNP-drug interactions was performed for all SNPs in the significant regions and all SNPs were analyzed for association with DD within conditioning intensity. In the MA group, we tested for interactions between each SNP and total body irradiation (TBI). We combined P-values for Cohorts 1 and 2 using a fixed effects model weighted by effect size and standard error (Pmeta).

None of the recipient or donor variants showed any significant interactions at Pmeta <.05 with MA/RIC. The stratified analysis showed the main effects of the SNPs were of similar magnitude and direction of effect in both of the MA and RIC groups and there was no evidence of heterogeneity between groups. The TBI*SNP interaction analyses showed no significant associations with DD. Furthermore, the DD SNP associations did not differ by early or advanced stage disease (Table). We present interaction and stratified analyses of our most significant association results. Importantly, all three regions contain genome-wide associated SNPs that have a direct regulatory effect on the expression of multiple genes across multiple tissues (including blood) via cis-expression quantitative trait loci (cis-eQTL) with genes in the region on Chromosome 9 shown to be associated with OS of leukemia patients.

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Our results demonstrate that conditioning intensity does not modify the effect of these donor SNPs in our GW significant regions on DD and this effect is uniform in early and advanced disease. This indicates that these findings associated with the DD variants are robust and generalizable to the HLA matched unrelated donor BMT population irrespective of disease status or conditioning intensity. Importantly we are now undertaking SNP*drug interaction analyses to assess our existing associations for other outcomes but to also identify new associations at the genome-wide level.

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Genetic Associations with Day +100 Transplant Related Mortality (TRM) after HLA-Matched Unrelated Donor (MUD) Blood and Marrow Transplantation (DISCOVeRY-BMT Study)

Abbas Rizvi, Ezgi Karaesmen, Leah Preus, Philip L. McCarthy, Marcelo Pasquini, Sachidanand Singh, Sandeep Kumar Singh, Kenan Onel, Xiaochun Zhu, Stephen Spellman, Christopher A. Haiman, Daniel O. Stram, Loreall Pooler, Xin Sheng, Qianqian Zhu, Li Yan, Qian Liu, Qiang Hu, Song Liu, Alyssa I. Clay-Gilmour, Sebastiano Battaglia, David Tritchler, Theresa Hahn, Lara E Sucheston-Campbell

About 40% of patients die of various causes before 1-year post-MUD BMT with almost half of these deaths occurring in the first 100 days post-transplant. We performed a genome-wide association study (GWAS) of survival outcomes within 1 year in the DISCOVeRY-BMT study of patients treated for AML, MDS or ALL and reported to CIBMTR from 2000-2011 and their 8/8 HLA-MUD. We previously presented our results of OS, PFS, TRM, death due to disease and death due to GvHD within 1 year-post-BMT. The purpose of the current work is to test these previously reported variants in a sensitivity analysis of day +100 outcomes.

We selected all unique variants showing an association of P<1x10-5 with 1 year TRM, OS or GvHD death. Each SNP was measured for its association in recipient or donor genomes with these outcomes at day +100 using Cox proportional hazard models. P-values from cohorts 1 and 2 (2,888 European Americanpatients) were combined using a fixed effects model. We report on results for combined P-value (Pmeta)<5x10-8 at day +100.

We detected several regions of association significant with day +100 death due to GvHD, TRM and OS at the genome-wide level on multiple chromosomes in all patients and those with ALL only. While the correlation between day +100 and 1 year for corresponding outcomes is strong (ρ2>.9), none of these day +100 GW significant regions were associated at the genome-wide level with survival outcomes at 1 year.The donor regions on chromosomes 2 and 3 represent our largest areas of association with death due toGvHD and TRM in the full DISCOVeRY-BMT cohort (Figure and Table). The chromosome 2 region containsvariants that are likely to affect transcription factor binding and correlate with expression of a number ofnearby genes. On chromosome 3, our associated variants are in strong linkage disequilibrium with codingvariants in TRPC1 (encodes an ion channel protein) and modify expression of this gene in blood. Ourlargest and most significant ALL specific association is seen in overall survival on chromosome 9 (Figureand Table) and has been previously identified as genome-wide significant in a large study of childhoodonset asthma.

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The sensitivity analyses of genetic associations with day +100 survival outcomes following transplant may be the first clue that non-HLA donor genetic effects emerge early and many associations exert a consistent effect during the first year post-BMT. Over half of GvHD deaths and TRM occurs in the first 100 days in the DISCOVeRY-BMT cohorts, thus it is not surprising our findings are consistent between day +100 and 1-year. However, from the figure it is also apparent that genetic effects of cause-specific death need tobe considered at different time points (e.g. early and later) due to differential kinetics of cause specificmortality over time. For this reason, a GWAS of day +100 TRM (and specific causes of death) and OS isunderway.

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Chromosome 6 Loss-of-heterozygosity in Pre-transplant Blood Samples of Patients with Severe Aplastic Anemia is Associated with Lower Risk of Acute Graft-versus-Host Disease

Shahinaz M Gadalla, Weiyin Zhou, Tao Wang, Youshitaka Zaimoku, Shinji Nakao, Laurie Burdette, Amanda Willis, Medhat Askar, Michael Haagenson, Meredith Yeager, Youjin Wang, Stephen R. Spellman, Stephen J. Chanock, Stephanie J. Lee, MD, and Sharon A. Savage

Introduction: Recent studies identified a subset of patients with severe aplastic anemia (SAA) with acquired clonal loss-of-heterozygosity of the HLA locus on chromosome 6p (6pLOH), an alteration that could allow escape from immune surveillance. Here, we examined the presence of this alteration in pre-transplant blood samples of patients with SAA and evaluated its effect on patient risk of acute graft-versus-host disease (aGvHD) after unrelated donor hematopoietic cell transplant (HCT).

Methods: We used single nucleotide polymorphism (SNP) array genotyping to identify chromosomal alterations in pre-HCT blood samples of 528 SAA patients who received unrelated donor HCT between 1998-2011. Samples and clinical data were provided by the Center for International Blood and Marrow Transplant Research (CIBMTR®) database and biorepository. Findings were validated in a cohort of 161 SAA patients from the Japanese Marrow Donor Program (JMDP) who received unrelated donor HCT between 1993-2011; 6pLOH was identified using digital droplet PCR in the Japanese cohort. Cox proportional hazard models were used to calculate the hazard ratios (HR) and 95% confidence intervals (CI) of aGvHD associated with 6pLOH.

Results: The median age at HCT was 25.5 and 22.7 years in the CIBMTR and JMDP cohorts, respectively. All the JMDP patients received bone marrow grafts versus 79% for the CIBMTR cohort, and were more likely to receive reduced intensity conditioning (85% vs. 47%, p<0.0001). 6pLOH was identified in 8% and 12% of the CIBMTR and JMDP SAA patients, respectively. The presence of 6pLOH was marginally associated with male sex (67% vs. 52%, p=0.05) and year of HCT (p=0.03) in the CIBMTR cohort and with age at HCT (mean=16 vs. 24 years, p=0.04) and time between AA diagnosis and HCT in the JMDP cohort (14 vs. 57 months, p<0.0001).

In the CIBMTR cohort, patients with 6pLOH had statistically significant lower cumulative incidence (CI) of aGvHD grade II-IV (16% vs. 30% at 100 days, p=0.04) and grade III-IV (5% vs. 17%, p=0.001). Multivariable analysis, adjusted for sex and year at HCT showed similar results, although not statistically significant. The HR for aGvHD grade II-IV=0.55 (95% CI=0.24-1.25), for aGvHD grade III-IV= 0.34 (95% CI=0.08-1.40). Multivariable models from the JMDP cohort, adjusted for age and time between AA diagnosis and HCT showed consistent results for aGvHD grade II-IV (HR=0.50, 95% CI=0.11-2.19). We do

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not report aGvHD grade III-IV because of small event count (n=5). No statistical significant associations in the other tested endpoints (overall survival and chronic GvHD) were observed for either cohorts.

In a combined analysis, multivariable models adjusted for degree of HLA matching, time between AA diagnosis and HCT, GvHD prophylaxis, year at HCT, and stratified on stem cell source, showed a statistically significant lower risk of aGvHD II-IV (HR=0.47, 95% CI=0.23-0.96, p=0.04 in all patients, and HR=0.36, 95% CI=0.15-0.89, p=0.02 in patients who received bone marrow grafts).

Conclusion: Pre-HCT clonal presence of 6pLOH in patients with SAA is associated with a reduction in risk for acute GvHD. This alteration identifies a subset of SAA patients with potentially lower HCT-risk, and should be further studied to determine whether it could guide therapeutic decisions for those patients.

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Clonal alterations and survival after unrelated donor allogeneic hematopoietic stem cell transplant in patients with Fanconi anemia

Youjin Wang, Weiyin Zhou, Tao Wang, Stephen R. Spellman, Michael Haagenson, Meredith Yeager, Blanche P. Alter, Stephanie J. Lee, Sharon A. Savage, and Shahinaz M. Gadalla

Background: Studies of bone marrow cell clonal alterations in patients with Fanconi anemia (FA), a cancer-prone inherited bone marrow failure (BMF) syndrome, have focused on their role in progression from BMF to myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). The role of such alterations on patient outcomes after hematopoietic cell transplantation (HCT) is unknown.

Methods: Using genome-wide SNP arrays, we investigated clonal alterations in pre-HCT blood samples from 73 FA patients who received allogeneic unrelated HCT between 1991-2007. Clinical data and blood samples were available through the Center for International Blood and Marrow Transplant Research. The association of pre-HCT clonal alterations with overall survival (OS) after HCT were evaluated in univariate analysis using the Kaplan-Meier method and in multivariable analysis using Cox proportional hazard models.

Results: Median age at HCT was 10.6 years (range=2.5-28.8), 53% of the patients were males, 86% received bone marrow grafts, 64% received reduced intensity or non-myeloablative regimens, and 41% had an 8/8 HLA matched donor. Clonal alterations were detected in 16 patients (22%). The most frequent alterations were copy-loss in chromosome 7 (chr7-; N=7, 10%), followed by copy-gain in chromosome 1 (chr1+; N=6, 8%) and in chromosome 3 (chr3+; N=6, 8%). No statistically significant demographic or transplant-related differences were observed in those with or without chromosomal alterations. In univariate analyses, chr1+ was statistically significantly associated with inferior survival (1-year OS of patients with and without ch1+ was 17 vs. 43%, respectively, log-rank p=0.03). Similar results were observed in patients with chr3+, 1-yr OS=0 with chr3+ vs. 45% without chr3+ (log-rank p=0.003). Acute GvHD was the mostcommon cause of death in patients with ch1+ or chr3+ (33.3% of all deaths vs. 4.4% in patientswithout alterations, p=0.03). No statistically significant difference in OS was noted with chr7-, 1-yr OS= 29% with chr7- vs. 42%without (p=0.57). Multivariable analysis adjusted for age andyear of transplant showed that patients with pre-HCT copy-gain in chr1 or chr3 had a nearly

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three-fold excess risk of dying after HCT (hazard ratio (HR)=2.69, 95% confidence interval [CI]=1.22-5.91, p=0.01); The HRs in non-myeloablative/reduced intensity and myeloablative conditioning were 3.84 (p=0.02) and 1.47(p=0.53), respectively. No association between chr7 loss and OS was seen in multivariable analyses (HR=1.18, 95% CI=0.46-3.03, p=0.74).

Conclusion: Pre-HCT clonal copy-gain in chr1 or chr3 was associated with worse post-transplant survival in patients with FA. The stronger association with non-myeloablative/reduced intensity conditioning suggest a possible role for mixed chimerism. Larger studies of post-HCT outcome in relation to chromosomal aberrations are warranted.

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Donor Lymphocyte Cell-Specific Telomere Length and Causes of Death after Unrelated Hematopoietic Cell Transplant in Patients with Marrow Failure

Shahinaz M. Gadalla, Geraldine Aubert, Tao Wang, Michael Haagenson, Stephen R. Spellman, Lingxiao Wang, Hormuzd Katki, Stephanie J. Lee, and Sharon A. Savage

Introduction: Several recent studies indicated that longer donor telomere length provides a survival advantage to patients receiving unrelated donor hematopoietic cell transplant (HCT) for severe aplastic anemia (SAA). However, the cause behind this observation is still unclear.

Method: From the Center for International Blood and Marrow Transplant Research (CIBMTR®) database and biorepository, we identified 197 marrow failure patients who received unrelated donor HCT between 1988-2004, and for whom donor cryopreserved peripheral blood mononuclear cells were available. We used flow cytometry and fluorescence in situ hybridization (Flow FISH) analysis to measure telomere length in 4 lymphocyte cell subtypes: naïve enriched T-cells (CD45RA+CD20-), memory enriched T-cells (CD45RA-CD20-), NK-fully differentiated T cells (CD45RA+CD57+), and B cells (CD45RA+CD20+). We used competing risks survival regression for cause-specific death analysis. Cause of death categories used as reported to CIBMTR by the transplant centers. We focused on deaths from infections, graft-versus-host disease (GvHD), and graft failure, each modeled separately, with deaths from causes other than that of interest representing competing events. Models were adjusted for donor age, HLA matching, disease subtype, Karnofsky score, year of HCT, and prior SAA therapy. Median follow-up of the study was 60 months.

Results: All patients in this study received bone marrow grafts, 32% had inherited marrow failure syndrome, and 49% received myeloablative conditioning. During follow-up, 135 died, 26% of the deaths were from infections, 18% from GvHD, 10% from primary or secondary graft failure, according to reporting from the transplant center. Longer donor telomere lengths in B cells (HR=0.63, 95% CI=0.46-0.87, p=0.006), and possibly NK- fully differentiated T cells (HR=0.7, 95% CI=0.51-0.97, p=0.03) were associated with lower risk of infection-related death. No statistically significant associations were seen with telomere length of the naïve or memory T-cells in relation to death due to infection. Donor telomere lengths in any of the tested lymphocyte subsets were not associated with death caused by GvHD or graft failure (p<0.05).

Conclusions: Longer donor telomere length protects SAA patients from post-HCT infection-related deaths. Validating these results in recent and larger cohort is warranted.

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Genomic mechanisms of SES-related outcome disparities in hematopoietic cell transplantation

Jennifer M. Knight, J. Douglas Rizzo, Tao Wang, Naya He, Brent R. Logan, Steve Spellman, Stephanie J. Lee, Steve W. Cole

1. Identify the influence of SES on transcriptome profiles.2. Explain what the conserved transcriptional response to adversity (CTRA) gene expression profileis.3. Distinguish between SES and disease-related hematopoietic changes and impacts on outcome.

Background: Low socioeconomic status (SES) is associated with worse overall survival (OS) and higher transplant-related mortality (TRM) in unrelated donor (URD) hematopoietic cell transplantation (HCT) recipients, but the biological mechanisms of SES-related outcome gradients remain poorly understood. Our previous research on URD-HCT in patients with acute myelogenous leukemia (AML) linked low SES to increased expression of a leukocyte transcriptome program known as the conserved transcriptional response to adversity (CTRA). The CTRA is characterized by increased expression of inflammatory genes and decreased expression of Type I interferon- and antibody-related transcripts, and is driven in part by myelopoietic up-regulation of CD16- classical monocytes and down-regulation of CD16+ non-classical monocytes. The current study re-examined this relationship in a larger cohort suitable for multivariate analyses.

Methods: In 261 adults receiving URD-HCT for AML in CR1 or CR2, pre-transplant peripheral blood mononuclear cells (PBMCs) were assessed for 1) CTRA expression (using an a priori-defined composite of 52 genes including 19 pro-inflammatory, 29 interferon-related, and 3 antibody-related genes), and 2) classical monocyte activation (using bioinformatic transcript origin analysis [TOA] of genes showing > 1.2-fold differential expression in the lowest income quartile). Survival analyses tested associations between quartiled gene expression parameters and outcomes (OS, LFS, TRM, relapse, neutrophil engraftment, platelet recovery, and a/cGVHD).

Results: In pre-transplant PBMCs, low-SES individuals showed CTRA-typical increases in classical monocyte activation (p<.001) and decreases in activation of non-classical monocytes (p<.05). Decreased non-classical monocyte activation was associated with greater relapse at 3-years post-HCT (p=.01). Although not predicted a priori, decreased activation of classical monocytes was also associated with increased relapse, resulting in a U-shaped relationship with TOA scores (p=.02; Figure 1). Similarly, the highest and lowest quartiles of the CTRA pro-inflammatory gene composite score demonstrate increased 3-year relapse (p<.01; Figure 2) and decreased LFS (p=.02; Figure 3).

Conclusions: Socioeconomic adversity is associated with altered myeloid lineage gene regulation, and those alterations are in turn associated with relapse and decreased LFS in multivariate survival

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analysis. The U-shaped relationship between gene regulation and outcomes warrants further investigation to evaluate whether reduced representation of total monocytes additionally contributes to adverse clinical outcomes in this cohort.

Figure 1. Cumulative incidence of relapse by cell TOA score

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Figure 2. Cumulative incidence of relapse by pro-inflammatory CTRA score

Figure 3. Probability of leukemia-free survival by pro-inflammatory CTRA score

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Donor SNPs in IL1RL1, Strongly Correlated with Serum sST2 Concentration, Significantly Associate with Risk of Acute GvHD: Implications for Donor Selection

Lara Sucheston-Campbell, Sandeep Kumar Singh, Ezgi Karaesmen, Abbas Rizvi, Sachidanand Singh, Tao Wang, Michael Haagenson, Alex Dile, Leah Preus, Stephen R. Spellman, Stephanie J. Lee, Theresa E. Hahn, and Sophie Paczesny

Elevated soluble STimulation-2 (sST2) levels in plasma/serum have been associated with increased risk for therapy-resistant GVHD in several cohorts. In a genome-wide association study (GWAS) of 2,991 Framingham offspring cohort participants, 390 single nucleotide polymorphisms (SNPs) across the region containing IL1RL1 (Chromosome 2, 102.72 to 103.25 Mb), which encodes ST2, demonstrated GWA (p<5x10-8) with sST2 plasma/serum concentrations. In a first study attempting to correlate proteomics and functional genomics after HCT, we measured the relationship of these GWA SNPs with sST2 concentrations in AML and MDS patients and their HLA-matched unrelated donors and then assessed if donor SNPs associated with risk of acute GvHD, with the intention of identifying functional donor genetic markers that could potentially aid in donor selection.

We first measured plasma and serum sST2 concentrations in a subset of AML and MDS patients (N=759) and their donors (N=757) from DISCOVeRY-BMT, a GWAS of over 3,500 recipient-unrelated donor pairs reported to CIBMTR between 2000-2011. We selected 53 uncorrelated SNPs that captured the genetic variation in the IL1RL1 region to test for association with sST2 concentrations. Importantly these variants were also selected as they are significantly more likely to affect transcription factor binding and gene expression than those in the overall genome (Fig. 1). We successfully validated 51/53 Framingham associations with sST2 concentrations in our donor BMT population (Table, Fig. 2) and replicated 39/53 SNP associations in our AML and MDS patients (top 10 associations are in Table) at p<.05. The most significant donor association, the A allele at rs950880 (p=6.02 x 10-53), is also the most significant association in Framingham (p=7.1 x 10-94, Table).

Competing risk models of donor SNP associations with grade II-IV acute GvHD in AML and MDS patients, identified 2 associations in IL1R2, both whole blood eQTLs for IL18R1. Rs2302612, a missense SNP, likely affects transcription factor binding (Regulomedb score=2, Fig. 1) correlates with lower sST2 concentration (p=3.4 x 10-21) and lower risk of acute GVHD (HRmeta=.82, 95%CI=.69-.97, Pmeta=.02). The donor C allele in rs3755285 is significantly correlated with lower sST2 levels (p=1.3 x 10-4) and reduces risk of grade II-IV acute GVHD (HRmeta=.83, 95%CI=.72-.97, Pmeta=.02). These associations show that our functional biomarkers directly correlated to an important clinical outcome. Also, it may be relevant for donor selection, that the C allele frequency at both SNPs varies by race between 11-55%.

To better determine the collective impact of this sST2 associated genomic region on patient outcomes we are creating individual donor risk scores based on a donor’s total number of alleles that increase

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sST2 concentrations. We will then be able to relate these risk scores (or risk groups) to a patient’s risk of GvHD.

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